Merge pull request #26 from geekwenjie/master

合并

README.md

@@ -6,7 +6,7 @@
 	<em>像Hutool一样简单易用的Java AI工具箱</em>
 </p>
 <p align="center">
-	👉 <a href="http://numberone.ink/">http://numberone.ink/</a> 👈
+	👉 <a href="http://smartjavaai.cn/">http://smartjavaai.cn/</a> 👈
 </p>
 <p align="center">
 	<a target="_blank" href="https://central.sonatype.com/artifact/ink.numberone/smartjavaai-all">
@@ -31,7 +31,7 @@
 
 -------------------------------------------------------------------------------
 
-[**开发文档**](http://doc.numberone.ink)
+[**开发文档**](http://doc.smartjavaai.cn)
 
 -------------------------------------------------------------------------------
 
@@ -68,7 +68,7 @@ SmartJavaAI是专为JAVA 开发者打造的一个功能丰富、开箱即用的
       </td>     
       <td>
         <div align="center">
-        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/face/face4.jpg" width = "400px"/>
+        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/face5point.png" width = "500px"/>
         </div>
       </td>
     </tr>          
@@ -81,7 +81,7 @@ SmartJavaAI是专为JAVA 开发者打造的一个功能丰富、开箱即用的
       </td>     
       <td>
         <div align="center">
-        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/face1-1.jpg" width = "400px"/>
+        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/face1-1.jpg" width = "500px"/>
         </div>
       </td>
     </tr>  
@@ -93,7 +93,7 @@ SmartJavaAI是专为JAVA 开发者打造的一个功能丰富、开箱即用的
       </td>     
       <td>
         <div align="center">
-        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/idcard.png" width = "400px"/>
+        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/idcard.png" width = "500px"/>
         </div>
       </td>
     </tr>
@@ -109,7 +109,7 @@ SmartJavaAI是专为JAVA 开发者打造的一个功能丰富、开箱即用的
       </td>     
       <td>
         <div align="center">
-        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/face1-n.png" width = "400px"/>
+        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/face1-n.png" width = "500px"/>
         </div>
       </td>
     </tr>  
@@ -126,7 +126,7 @@ SmartJavaAI是专为JAVA 开发者打造的一个功能丰富、开箱即用的
       </td>     
       <td>
         <div align="center">
-        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/face_attribute.jpg" width = "400px"/>
+        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/face_attribute.jpg" width = "500px"/>
         </div>
       </td>
     </tr> 
@@ -139,7 +139,7 @@ SmartJavaAI是专为JAVA 开发者打造的一个功能丰富、开箱即用的
       </td>     
       <td>
         <div align="center">
-        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/liveness2.jpg" width = "400px"/>
+        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/liveness2.jpg" width = "500px"/>
         </div>
       </td>
     </tr> 
@@ -152,7 +152,7 @@ SmartJavaAI是专为JAVA 开发者打造的一个功能丰富、开箱即用的
       </td>     
       <td>
         <div align="center">
-        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/emotion.jpg" width = "400px"/>
+        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/emotion.jpg" width = "500px"/>
         </div>
       </td>
     </tr> 
@@ -177,7 +177,12 @@ SmartJavaAI是专为JAVA 开发者打造的一个功能丰富、开箱即用的
       </td>     
       <td>
         <div align="center">
-        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/objectdect/object_detection_detected.jpg" width = "400px"/>
+        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/objectdect/object_detect_1.jpeg" width = "500px"/>
+        </div>
+      </td>
+      <td>
+        <div align="center">
+        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/objectdect/object_detection_detected.jpg" width = "500px"/>
         </div>
       </td>
     </tr>
@@ -289,7 +294,7 @@ SmartJavaAI是专为JAVA 开发者打造的一个功能丰富、开箱即用的
       </td>     
       <td>
         <div align="center">
-        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/ocr/general_ocr_002_recognized.png" width = "400px"/>
+        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/ocr/general_ocr_002_recognized.png" width = "500px"/>
         </div>
       </td>
     </tr> 
@@ -302,7 +307,7 @@ SmartJavaAI是专为JAVA 开发者打造的一个功能丰富、开箱即用的
       </td>     
       <td>
         <div align="center">
-        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/ocr/table.jpg" width = "400px"/>
+        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/ocr/table.jpg" width = "500px"/>
         </div>
       </td>
     </tr>
@@ -316,44 +321,14 @@ SmartJavaAI是专为JAVA 开发者打造的一个功能丰富、开箱即用的
       </td>     
       <td>
         <div align="center">
-        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/ocr/plate_recognized.jpg" width = "400px"/>
+        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/ocr/plate_recognized.jpg" width = "500px"/>
         </div>
       </td>
-    </tr>
-  <tr>
       <td>
-        <div align="left">
+        <div align="center">
-          <p>身份证识别<br>(IDCard Recognition)</p>
+        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/ocr/plate_recognized2.jpg" width = "500px"/>
-          - 正面识别 <br>
         </div>
       </td>
-    <td>
-      <div align="center">
-        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/ocr/idcard/idcard_front.png" width="500px"/>
-        <pre align="left">{
-      "name": "小氧",
-      "gender": "女",
-      "ethnicity": "汉",
-      "idNumber": "430602200010108888",
-      "birthday": "2000-10-10",
-      "address": "湖南省岳阳市岳阳楼区金鄂中路456号"
-    }</pre>
-      </div>
-    </td>
-    </tr>
-  <tr>
-      <td>
-        <div align="left">
-          <p>身份证识别<br>(IDCard Recognition)</p>
-          - 反面识别 <br>
-        </div>
-      </td>     
-     <td>
-      <div align="center">
-        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/ocr/idcard/idcard_back.png" width="500px"/>
-        <pre align="left">{"issuingAuthority":"杭州市公安局江干分局","validFrom":"2015-11-05","validTo":"2025-11-05"}</pre>
-      </div>
-    </td>
     </tr>
     <tr>
       <td>
@@ -364,7 +339,7 @@ SmartJavaAI是专为JAVA 开发者打造的一个功能丰富、开箱即用的
       </td>     
       <td>
         <div align="center">
-        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/translate/translate.png" width = "400px"/>
+        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/translate/translate.png" width = "500px"/>
         </div>
       </td>
     </tr>
@@ -378,7 +353,7 @@ SmartJavaAI是专为JAVA 开发者打造的一个功能丰富、开箱即用的
       </td>     
       <td>
         <div align="center">
-        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/speech/asr.png" width = "400px"/>
+        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/speech/asr.png" width = "500px"/>
         </div>
       </td>
     </tr>
@@ -390,7 +365,7 @@ SmartJavaAI是专为JAVA 开发者打造的一个功能丰富、开箱即用的
       </td>     
       <td>
         <div align="center">
-        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/speech/tts.jpg" width = "400px"/>
+        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/speech/tts.jpg" width = "500px"/>
         </div>
       </td>
     </tr>
@@ -405,7 +380,7 @@ SmartJavaAI是专为JAVA 开发者打造的一个功能丰富、开箱即用的
       </td>     
       <td>
         <div align="center">
-        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/vision/clip.png" width = "400px"/>
+        <img src="https://cdn.jsdelivr.net/gh/geekwenjie/SmartJavaAI-Site/images/vision/clip.png" width = "500px"/>
         </div>
       </td>
     </tr>
@@ -472,7 +447,6 @@ SmartJavaAI是专为JAVA 开发者打造的一个功能丰富、开箱即用的
   - 支持通用文字识别，通用手写字识别
   - 支持表格识别
   - 支持中文车牌识别：单层/双层检测，颜色识别，支持12种中文车牌
-  - 支持身份证识别：支持身份证正反面字段提取、方向矫正与结构化解析
 - **机器翻译**
   - 集成NLLB-200模型：支持200+语言互相翻译
 - **语音识别(ASR)**
@@ -526,8 +500,7 @@ SmartJavaAI是专为JAVA 开发者打造的一个功能丰富、开箱即用的
 ### 1、环境要求
 
 - Java 版本：**JDK 8或更高版本**
-- 操作系统：Windows 64 位 / Linux / macOS M1
+- 操作系统：不同模型支持的系统不一样，具体请查看[文档](http://doc.smartjavaai.cn)
-- CPU架构：x86_64、ARM64（aarch64）
 
 ### 2、Maven
 
@@ -535,13 +508,13 @@ SmartJavaAI是专为JAVA 开发者打造的一个功能丰富、开箱即用的
  
 ⚠️ **注意：不推荐直接引入全部依赖**，更推荐根据实际需求，按功能模块单独引入，避免引入不必要的包。
 
-详细引入方式请查看 [文档](http://doc.numberone.ink/install.html)、或查看[示例代码](https://gitee.com/dengwenjie/SmartJavaAI/tree/master/examples)
+详细引入方式请查看 [文档](http://doc.smartjavaai.cn/install.html)、或查看[示例代码](https://gitee.com/dengwenjie/SmartJavaAI/tree/master/examples)
 
 ```xml
 <dependency>
     <groupId>cn.smartjavaai</groupId>
     <artifactId>all</artifactId>
-    <version>1.1.2</version>
+    <version>1.1.1</version>
 </dependency>
 ```
 
@@ -575,7 +548,7 @@ SmartJavaAI是专为JAVA 开发者打造的一个功能丰富、开箱即用的
 
 ### 4、文档地址
 
-[开发文档](http://doc.numberone.ink)
+[开发文档](http://doc.smartjavaai.cn)
 
 ### 5、模型简介及下载
 
@@ -903,3 +876,4 @@ SmartJavaAI是专为JAVA 开发者打造的一个功能丰富、开箱即用的
 6、等待维护者合并
 
 
+

all/pom.xml

@@ -6,11 +6,11 @@
     <parent>
         <groupId>cn.smartjavaai</groupId>
         <artifactId>smartjavaai-parent</artifactId>
-        <version>1.1.2</version>
+        <version>1.1.1</version>
     </parent>
 
     <artifactId>all</artifactId>
-    <version>1.1.2</version>
+    <version>1.1.1</version>
     <name>${project.artifactId}</name>
     <description>SmartJavaAI</description>
     <url>https://github.com/geekwenjie/SmartJavaAI</url>

bom/pom.xml

@@ -6,10 +6,10 @@
     <parent>
         <groupId>cn.smartjavaai</groupId>
         <artifactId>smartjavaai-parent</artifactId>
-        <version>1.1.2</version>
+        <version>1.1.1</version>
     </parent>
 
-    <version>1.1.2</version>
+    <version>1.1.1</version>
     <artifactId>bom</artifactId>
     <name>bom</name>
     <description>统一版本管理的 BOM 包，同时支持 import 和全量依赖</description>

common/pom.xml

@@ -6,7 +6,7 @@
     <parent>
         <groupId>cn.smartjavaai</groupId>
         <artifactId>smartjavaai-parent</artifactId>
-        <version>1.1.2</version>
+        <version>1.1.1</version>
     </parent>
 
     <name>common</name>

examples/face-example/README.md

@@ -59,6 +59,6 @@ src/main/java/smartai/examples/face/
 ## 📄 文档
 
 有关完整使用说明，请查阅 SmartJavaAI 官方文档：
-[http://doc.numberone.ink](http://doc.numberone.ink)
+[http://doc.smartjavaai.cn](http://doc.smartjavaai.cn)
 
 ---

examples/face-example/pom.xml

@@ -12,7 +12,7 @@
         <maven.compiler.source>11</maven.compiler.source>
         <maven.compiler.target>11</maven.compiler.target>
         <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
-        <smartjavaai.version>1.1.2</smartjavaai.version>
+        <smartjavaai.version>1.1.1</smartjavaai.version>
         <!--如果打包运行，需要替换成你的main-->
         <exec.mainClass>smartai.examples.face.FaceDemo</exec.mainClass>
 

examples/face-example/src/main/java/smartai/examples/face/attribute/FaceAttributeDetDemo.java

@@ -33,7 +33,7 @@ import java.util.List;
 /**
  * 人脸属性检测demo
  * 模型下载地址：https://pan.baidu.com/s/10l22x5fRz_gwLr8EAHa1Jg?pwd=1234 提取码: 1234
- * 文档地址：http://doc.numberone.ink/
+ * 文档地址：http://doc.smartjavaai.cn/
  * @author dwj
  */
 @Slf4j

examples/face-example/src/main/java/smartai/examples/face/expression/ExpressionRecDemo.java

@@ -51,7 +51,7 @@ import java.util.List;
  * 表情识别demo
  * 支持识别7种表情：neutral（中性）、happy（高兴）、sad（悲伤）、surprise（惊讶）、fear（恐惧）、disgust（厌恶）、anger（愤怒）
  * 模型下载地址：https://pan.baidu.com/s/10l22x5fRz_gwLr8EAHa1Jg?pwd=1234 提取码: 1234
- * 文档地址：http://doc.numberone.ink/
+ * 文档地址：http://doc.smartjavaai.cn/
  * @author dwj
  */
 @Slf4j

examples/face-example/src/main/java/smartai/examples/face/facedet/FaceDetDemo.java

@@ -43,7 +43,7 @@ import java.nio.file.Paths;
 /**
  * 人脸检测模型demo
  * 模型下载地址：https://pan.baidu.com/s/1d2YlJ2YOdGn3Y-AegyAhmQ?pwd=1234 提取码: 1234
- * 文档地址：http://doc.numberone.ink/
+ * 文档地址：http://doc.smartjavaai.cn/
  * @author dwj
  */
 @Slf4j

examples/face-example/src/main/java/smartai/examples/face/facerec/FaceRecDemo.java

@@ -37,7 +37,7 @@ import java.util.List;
 /**
  * 人脸识别模型demo
  * 模型下载地址：https://pan.baidu.com/s/10l22x5fRz_gwLr8EAHa1Jg?pwd=1234 提取码: 1234
- * 文档地址：http://doc.numberone.ink/
+ * 文档地址：http://doc.smartjavaai.cn/
  * @author dwj
  */
 @Slf4j
@@ -78,7 +78,7 @@ public class FaceRecDemo {
      * 获取人脸检测模型（高精度模型）
      * 注意事项：
      * 1、高精度模型，识别准确度高，速度慢
-     * 2、具体其他模型参数可以查看文档：http://doc.numberone.ink/face.html
+     * 2、具体其他模型参数可以查看文档：http://doc.smartjavaai.cn/face.html
      * @return
      */
     public FaceDetModel getProFaceDetModel(){
@@ -98,7 +98,7 @@ public class FaceRecDemo {
      * 获取人脸检测模型（极速模型）
      * 注意事项：
      * 1、极速模型，识别准确度低，速度快
-     * 2、具体其他模型参数可以查看文档：http://doc.numberone.ink/face.html
+     * 2、具体其他模型参数可以查看文档：http://doc.smartjavaai.cn/face.html
      * @return
      */
     public FaceDetModel getFastFaceDetModel(){
@@ -118,7 +118,7 @@ public class FaceRecDemo {
     /**
      * 获取人脸识别模型（高精度，速度慢）
      * 追求准确度可以使用
-     * 也可以使用其他模型，具体其他模型参数可以查看文档：http://doc.numberone.ink/face.html
+     * 也可以使用其他模型，具体其他模型参数可以查看文档：http://doc.smartjavaai.cn/face.html
      * @return
      */
     public FaceRecModel getFaceRecModel(){
@@ -140,7 +140,7 @@ public class FaceRecDemo {
     /**
      * 获取人脸识别模型（高速模型，精度一般）
      * 追求速度可以使用
-     * 也可以使用其他模型，具体其他模型参数可以查看文档：http://doc.numberone.ink/face.html
+     * 也可以使用其他模型，具体其他模型参数可以查看文档：http://doc.smartjavaai.cn/face.html
      * @return
      */
     public FaceRecModel getHighSpeedFaceRecModel(){
@@ -165,7 +165,7 @@ public class FaceRecDemo {
      */
     public FaceRecModel getFaceRecModelWithDbConfig(){
         FaceRecConfig config = new FaceRecConfig();
-        //高精度模型，速度慢,追求速度请更换高速模型，具体其他模型参数可以查看文档：http://doc.numberone.ink/face.html
+        //高精度模型，速度慢,追求速度请更换高速模型，具体其他模型参数可以查看文档：http://doc.smartjavaai.cn/face.html
         config.setModelEnum(FaceRecModelEnum.INSIGHT_FACE_IRSE50_MODEL);
         //模型路径，请下载模型并替换为本地路径：https://pan.baidu.com/s/10l22x5fRz_gwLr8EAHa1Jg?pwd=1234 提取码: 1234
         config.setModelPath("/Users/wenjie/Documents/develop/model/face_model/recognition/InsightFace/model_ir_se50.pt");
@@ -173,7 +173,7 @@ public class FaceRecDemo {
         config.setCropFace(true);
         //开启人脸对齐：适用于人脸不正的场景，开启将提升人脸特征准确度，关闭可以提升性能
         config.setAlign(true);
-        //指定人脸检测模型，可切换人脸检测模型（极速：getFastFaceDetModel，高精度：getProFaceDetModel），具体其他模型参数可以查看文档：http://doc.numberone.ink/face.html
+        //指定人脸检测模型，可切换人脸检测模型（极速：getFastFaceDetModel，高精度：getProFaceDetModel），具体其他模型参数可以查看文档：http://doc.smartjavaai.cn/face.html
         config.setDetectModel(getFaceDetModel());
         config.setDevice(device);
 
@@ -198,7 +198,7 @@ public class FaceRecDemo {
      */
     public FaceRecModel getFaceRecModelWithSQLiteConfig(){
         FaceRecConfig config = new FaceRecConfig();
-        //高精度模型，速度慢, 追求速度请更换高速模型，具体其他模型参数可以查看文档：http://doc.numberone.ink/face.html
+        //高精度模型，速度慢, 追求速度请更换高速模型，具体其他模型参数可以查看文档：http://doc.smartjavaai.cn/face.html
         config.setModelEnum(FaceRecModelEnum.INSIGHT_FACE_IRSE50_MODEL);
         //模型路径，请下载模型并替换为本地路径：https://pan.baidu.com/s/10l22x5fRz_gwLr8EAHa1Jg?pwd=1234 提取码: 1234
         config.setModelPath("/Users/wenjie/Documents/develop/model/face_model/recognition/InsightFace/model_ir_se50.pt");
@@ -206,7 +206,7 @@ public class FaceRecDemo {
         config.setCropFace(true);
         //开启人脸对齐：适用于人脸不正的场景，开启将提升人脸特征准确度，关闭可以提升性能
         config.setAlign(true);
-        //指定人脸检测模型，可切换人脸检测模型（极速：getFastFaceDetModel，高精度：getProFaceDetModel），具体其他模型参数可以查看文档：http://doc.numberone.ink/face.html
+        //指定人脸检测模型，可切换人脸检测模型（极速：getFastFaceDetModel，高精度：getProFaceDetModel），具体其他模型参数可以查看文档：http://doc.smartjavaai.cn/face.html
         config.setDetectModel(getFaceDetModel());
         config.setDevice(device);
 

examples/face-example/src/main/java/smartai/examples/face/liveness/LivenessDetDemo.java

@@ -52,7 +52,7 @@ import java.util.List;
 /**
  * 静态活体检测demo
  * 模型下载地址：https://pan.baidu.com/s/10l22x5fRz_gwLr8EAHa1Jg?pwd=1234 提取码: 1234
- * 文档地址：http://doc.numberone.ink/
+ * 文档地址：http://doc.smartjavaai.cn/
  * @author dwj
  */
 @Slf4j

examples/face-example/src/main/java/smartai/examples/face/quality/FaceQualityDetDemo.java

@@ -38,7 +38,7 @@ import java.nio.file.Paths;
 /**
  * 人脸质量评估 demo
  * 模型下载地址：https://pan.baidu.com/s/10l22x5fRz_gwLr8EAHa1Jg?pwd=1234 提取码: 1234
- * 文档地址：http://doc.numberone.ink/
+ * 文档地址：http://doc.smartjavaai.cn/
  * @author dwj
  */
 @Slf4j

examples/ocr-examples/README.md

@@ -61,6 +61,6 @@ src
 ## 📄 文档
 
 有关完整使用说明，请查阅 SmartJavaAI 官方文档：
-[http://doc.numberone.ink](http://doc.numberone.ink)
+[http://doc.smartjavaai.cn](http://doc.smartjavaai.cn)
 
 ---

examples/ocr-examples/pom.xml

@@ -12,9 +12,9 @@
         <maven.compiler.source>11</maven.compiler.source>
         <maven.compiler.target>11</maven.compiler.target>
         <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
-        <smartjavaai.version>1.1.2</smartjavaai.version>
+        <smartjavaai.version>1.1.1</smartjavaai.version>
         <!--如果打包运行，需要替换成你的main-->
-        <exec.mainClass>smartai.examples.ocr.common.OcrDetectionDemo</exec.mainClass>
+        <exec.mainClass>smartai.examples.ocr.common.OcrRecognizeDemo</exec.mainClass>
 
         <javacv.version>1.5.10</javacv.version>
 
@@ -90,33 +90,19 @@
             <groupId>cn.smartjavaai</groupId>
             <artifactId>ocr</artifactId>
             <exclusions>
-<!--                <exclusion>-->
+                <exclusion>
-<!--                    <groupId>org.openpnp</groupId>-->
+                    <groupId>com.microsoft.onnxruntime</groupId>
-<!--                    <artifactId>opencv</artifactId>-->
+                    <artifactId>onnxruntime</artifactId>
-<!--                </exclusion>-->
+                </exclusion>
-<!--                <exclusion>-->
-<!--                    <groupId>com.microsoft.onnxruntime</groupId>-->
-<!--                    <artifactId>onnxruntime</artifactId>-->
-<!--                </exclusion>-->
-<!--                <exclusion>-->
-<!--                    <groupId>org.bytedeco</groupId>-->
-<!--                    <artifactId>javacv</artifactId>-->
-<!--                </exclusion>-->
             </exclusions>
         </dependency>
 
-<!--        <dependency>-->
+        <dependency>
-<!--            <groupId>com.microsoft.onnxruntime</groupId>-->
+            <groupId>com.microsoft.onnxruntime</groupId>
-<!--            <artifactId>onnxruntime</artifactId>-->
+            <artifactId>onnxruntime</artifactId>
-<!--            <version>1.16.3</version>-->
+            <version>1.20.0</version>
-<!--            <scope>compile</scope>-->
+            <scope>runtime</scope>
-<!--        </dependency>-->
+        </dependency>
-
-<!--        <dependency>-->
-<!--            <groupId>org.openpnp</groupId>-->
-<!--            <artifactId>opencv</artifactId>-->
-<!--            <version>3.4.2-2</version>-->
-<!--        </dependency>-->
 
 
         <dependency>
@@ -127,10 +113,79 @@
         </dependency>
 
 
+        <!-- windows平台 (保留对应平台的配置，可以减小包大小)-->
+        <dependency>
+            <groupId>org.bytedeco</groupId>
+            <artifactId>javacpp</artifactId>
+            <version>${javacv.version}</version>
+            <classifier>${javacv.platform.windows-x86_64}</classifier>
+        </dependency>
+        <dependency>
+            <groupId>org.bytedeco</groupId>
+            <artifactId>ffmpeg</artifactId>
+            <version>6.1.1-1.5.10</version>
+            <classifier>${javacv.platform.windows-x86_64}</classifier>
+        </dependency>
+
+        <dependency>
+            <groupId>org.bytedeco</groupId>
+            <artifactId>openblas</artifactId>
+            <version>0.3.26-1.5.10</version>
+            <classifier>${javacv.platform.windows-x86_64}</classifier>
+        </dependency>
+
+        <dependency>
+            <groupId>org.bytedeco</groupId>
+            <artifactId>opencv</artifactId>
+            <version>4.9.0-1.5.10</version>
+            <classifier>${javacv.platform.windows-x86_64}</classifier>
+        </dependency>
+
+        <dependency>
+            <groupId>ai.djl.pytorch</groupId>
+            <artifactId>pytorch-native-cpu</artifactId>
+            <classifier>${djl.platform.windows-x86_64}</classifier>
+            <version>2.7.1</version>
+            <scope>runtime</scope>
+        </dependency>
 
 
 
+        <!-- linux x86 平台 (保留对应平台的配置，可以减小包大小)-->
+        <dependency>
+            <groupId>org.bytedeco</groupId>
+            <artifactId>javacpp</artifactId>
+            <version>${javacv.version}</version>
+            <classifier>${javacv.platform.linux-x86_64}</classifier>
+        </dependency>
+        <dependency>
+            <groupId>org.bytedeco</groupId>
+            <artifactId>ffmpeg</artifactId>
+            <version>6.1.1-1.5.10</version>
+            <classifier>${javacv.platform.linux-x86_64}</classifier>
+        </dependency>
 
+        <dependency>
+            <groupId>org.bytedeco</groupId>
+            <artifactId>openblas</artifactId>
+            <version>0.3.26-1.5.10</version>
+            <classifier>${javacv.platform.linux-x86_64}</classifier>
+        </dependency>
+
+        <dependency>
+            <groupId>org.bytedeco</groupId>
+            <artifactId>opencv</artifactId>
+            <version>4.9.0-1.5.10</version>
+            <classifier>${javacv.platform.linux-x86_64}</classifier>
+        </dependency>
+
+        <dependency>
+            <groupId>ai.djl.pytorch</groupId>
+            <artifactId>pytorch-native-cpu</artifactId>
+            <classifier>${djl.platform.linux-x86_64}</classifier>
+            <version>2.7.1</version>
+            <scope>runtime</scope>
+        </dependency>
 
         <!-- linux aarch64 平台 (保留对应平台的配置，可以减小包大小)-->
         <dependency>
@@ -168,16 +223,42 @@
             <scope>runtime</scope>
         </dependency>
 
-<!--        <dependency>-->
-<!--            <groupId>ai.djl.pytorch</groupId>-->
-<!--            <artifactId>pytorch-native-cpu-precxx11</artifactId>-->
-<!--            <classifier>linux-aarch64</classifier>-->
-<!--            <version>2.5.1</version>-->
-<!--            <scope>runtime</scope>-->
-<!--        </dependency>-->
 
+        <!-- macOS M系列 平台  (保留对应平台的配置，可以减小包大小)-->
+        <dependency>
+            <groupId>org.bytedeco</groupId>
+            <artifactId>javacpp</artifactId>
+            <version>${javacv.version}</version>
+            <classifier>${javacv.platform.macosx-arm64}</classifier>
+        </dependency>
+        <dependency>
+            <groupId>org.bytedeco</groupId>
+            <artifactId>ffmpeg</artifactId>
+            <version>6.1.1-1.5.10</version>
+            <classifier>${javacv.platform.macosx-arm64}</classifier>
+        </dependency>
 
+        <dependency>
+            <groupId>org.bytedeco</groupId>
+            <artifactId>openblas</artifactId>
+            <version>0.3.26-1.5.10</version>
+            <classifier>${javacv.platform.macosx-arm64}</classifier>
+        </dependency>
 
+        <dependency>
+            <groupId>org.bytedeco</groupId>
+            <artifactId>opencv</artifactId>
+            <version>4.9.0-1.5.10</version>
+            <classifier>${javacv.platform.macosx-arm64}</classifier>
+        </dependency>
+
+        <dependency>
+            <groupId>ai.djl.pytorch</groupId>
+            <artifactId>pytorch-native-cpu</artifactId>
+            <classifier>${djl.platform.osx-aarch64}</classifier>
+            <version>2.7.1</version>
+            <scope>runtime</scope>
+        </dependency>
 
 
     </dependencies>

examples/ocr-examples/src/main/java/smartai/examples/ocr/common/OcrDetectionDemo.java

@@ -30,7 +30,7 @@ import java.util.List;
 /**
  * OCR 文本检测 示例
  * 模型下载地址：https://pan.baidu.com/s/15Noz2xHQzqMQSl1B19BobQ?pwd=1234 提取码: 1234
- * 开发文档：http://doc.numberone.ink/
+ * 开发文档：http://doc.smartjavaai.cn/
  * @author dwj
  */
 @Slf4j

examples/ocr-examples/src/main/java/smartai/examples/ocr/common/OcrDirectionDetDemo.java

@@ -25,7 +25,7 @@ import java.util.List;
 /**
  * OCR 行文本方向检测 示例
  * 模型下载地址：https://pan.baidu.com/s/1MLfd73Vjdpnuls9-oqc9uw?pwd=1234 提取码: 1234
- * 开发文档：http://doc.numberone.ink/
+ * 开发文档：http://doc.smartjavaai.cn/
  * @author dwj
  */
 @Slf4j

examples/ocr-examples/src/main/java/smartai/examples/ocr/common/OcrRecognizeDemo.java

@@ -33,18 +33,12 @@ import java.io.File;
 import java.io.IOException;
 import java.nio.file.Path;
 import java.nio.file.Paths;
-import java.util.ArrayList;
 import java.util.List;
-import java.util.concurrent.CountDownLatch;
-import java.util.concurrent.ExecutorService;
-import java.util.concurrent.Executors;
-import java.util.concurrent.TimeUnit;
-import java.util.concurrent.atomic.AtomicInteger;
 
 /**
  * OCR 文本识别 示例
  * 模型下载地址：https://pan.baidu.com/s/1MLfd73Vjdpnuls9-oqc9uw?pwd=1234 提取码: 1234
- * 开发文档：http://doc.numberone.ink/
+ * 开发文档：http://doc.smartjavaai.cn/
  * @author dwj
  */
 @Slf4j
@@ -278,78 +272,6 @@ public class OcrRecognizeDemo {
         }
     }
 
-    /**
-     * 多线程文本识别
-     * 注意事项：
-     * 1、模型在外层统一创建，多个线程共享同一个模型实例，避免重复加载模型。
-     * 2、每个线程内部单独创建 Image 对象，避免共享图片对象带来的线程安全问题。
-     */
-    @Test
-    public void multiThreadRecognize() {
-        ExecutorService executorService = null;
-        try {
-            final OcrCommonRecModel recModel = getFastRecModel();
-            final OcrRecOptions options = new OcrRecOptions(false, true);
-            final String imagePath = "src/main/resources/ocr_1.jpg";
-
-            int threadCount = 5;
-            int taskCount = 20;
-            CountDownLatch countDownLatch = new CountDownLatch(taskCount);
-            AtomicInteger successCount = new AtomicInteger(0);
-            AtomicInteger failCount = new AtomicInteger(0);
-            List<String> errorMessages = new ArrayList<>();
-            executorService = Executors.newFixedThreadPool(threadCount);
-
-            long startTime = System.currentTimeMillis();
-            for (int i = 0; i < taskCount; i++) {
-                final int taskIndex = i;
-                executorService.submit(() -> {
-                    try {
-                        Image image = SmartImageFactory.getInstance().fromFile(imagePath);
-                        OcrInfo ocrInfo = recModel.recognize(image, options);
-                        successCount.incrementAndGet();
-                        log.info("线程：{}，任务：{}，识别结果：{}",
-                                Thread.currentThread().getName(),
-                                taskIndex,
-                                JSONObject.toJSONString(ocrInfo));
-                    } catch (Exception e) {
-                        failCount.incrementAndGet();
-                        String errorMsg = "任务" + taskIndex + "识别失败：" + e.getMessage();
-                        synchronized (errorMessages) {
-                            errorMessages.add(errorMsg);
-                        }
-                        log.error(errorMsg, e);
-                    } finally {
-                        countDownLatch.countDown();
-                    }
-                });
-            }
-
-            countDownLatch.await();
-            long costTime = System.currentTimeMillis() - startTime;
-            log.info("多线程识别完成，总任务数：{}，成功：{}，失败：{}，耗时：{} ms",
-                    taskCount, successCount.get(), failCount.get(), costTime);
-
-            if (!errorMessages.isEmpty()) {
-                log.error("失败详情：{}", JsonUtils.toJson(errorMessages));
-            }
-        } catch (Exception e) {
-            e.printStackTrace();
-        } finally {
-            if (executorService != null) {
-                executorService.shutdown();
-                try {
-                    if (!executorService.awaitTermination(10, TimeUnit.SECONDS)) {
-                        executorService.shutdownNow();
-                    }
-                } catch (InterruptedException e) {
-                    executorService.shutdownNow();
-                    Thread.currentThread().interrupt();
-                }
-            }
-        }
-    }
-
 
 
 

examples/ocr-examples/src/main/java/smartai/examples/ocr/idcard/IdCardRecDemo.java

@@ -1,216 +0,0 @@
-package smartai.examples.ocr.idcard;
-
-import ai.djl.modality.cv.Image;
-import ai.djl.util.JsonUtils;
-import cn.smartjavaai.common.config.Config;
-import cn.smartjavaai.common.cv.SmartImageFactory;
-import cn.smartjavaai.common.enums.DeviceEnum;
-import cn.smartjavaai.common.utils.ImageUtils;
-import cn.smartjavaai.ocr.config.DirectionModelConfig;
-import cn.smartjavaai.ocr.config.OcrDetModelConfig;
-import cn.smartjavaai.ocr.config.OcrRecModelConfig;
-import cn.smartjavaai.ocr.config.OcrRecOptions;
-import cn.smartjavaai.ocr.entity.IdCardBackInfo;
-import cn.smartjavaai.ocr.entity.IdCardFrontInfo;
-import cn.smartjavaai.ocr.entity.IdCardInfo;
-import cn.smartjavaai.ocr.entity.OcrInfo;
-import cn.smartjavaai.ocr.enums.CommonDetModelEnum;
-import cn.smartjavaai.ocr.enums.CommonRecModelEnum;
-import cn.smartjavaai.ocr.enums.DirectionModelEnum;
-import cn.smartjavaai.ocr.factory.OcrModelFactory;
-import cn.smartjavaai.ocr.idcard.DefaultIdCardRecognizer;
-import cn.smartjavaai.ocr.idcard.IdCardPreprocessListener;
-import cn.smartjavaai.ocr.model.common.detect.OcrCommonDetModel;
-import cn.smartjavaai.ocr.model.common.direction.OcrDirectionModel;
-import cn.smartjavaai.ocr.model.common.recognize.OcrCommonRecModel;
-import lombok.extern.slf4j.Slf4j;
-import org.junit.BeforeClass;
-import org.junit.Test;
-
-import java.io.IOException;
-import java.nio.file.Files;
-import java.nio.file.Path;
-import java.nio.file.Paths;
-
-/**
- * 身份证识别 demo
- * 使用说明：
- * 1、先下载 OCR 模型
- * 2、把下面的模型路径改成你自己的本地路径
- * 3、把身份证图片路径改成你自己的图片路径
- * 4、优先运行 recognizeFront() / recognizeBack() 查看结构化结果
- *
- * 模型下载地址：https://pan.baidu.com/s/1MLfd73Vjdpnuls9-oqc9uw?pwd=1234 提取码: 1234
- * 开发文档：http://doc.numberone.ink/
- * @author dwj
- */
-@Slf4j
-public class IdCardRecDemo {
-
-    // 设备类型
-    public static DeviceEnum device = DeviceEnum.CPU;
-
-    // 下载模型后，请替换成你自己的模型路径
-    private static final String DET_MODEL_PATH =
-            "/Users/wenjie/Documents/develop/model/ocr/PP-OCRv5_server_det_infer/PP-OCRv5_server_det.onnx";
-    private static final String REC_MODEL_PATH =
-            "/Users/wenjie/Documents/develop/model/ocr/PP-OCRv5_server_rec_infer/PP-OCRv5_server_rec.onnx";
-    private static final String DIRECTION_MODEL_PATH =
-            "/Users/wenjie/Documents/develop/model/ocr/PP-LCNet_x0_25_textline_ori_infer/PP-LCNet_x0_25_textline_ori_infer.onnx";
-
-    // 这里改成你自己的身份证图片路径
-    private static final String FRONT_IMAGE_PATH = "src/main/resources/idcard/idcard_front1.png";
-    private static final String BACK_IMAGE_PATH = "src/main/resources/idcard/idcard_back1.png";
-
-    @BeforeClass
-    public static void beforeAll() throws IOException {
-        // 修改缓存路径
-//        Config.setCachePath("/Users/xxx/smartjavaai_cache");
-    }
-
-    /**
-     * 获取文本检测模型
-     * @return
-     */
-    public OcrCommonDetModel getDetectionModel() {
-        OcrDetModelConfig config = new OcrDetModelConfig();
-        // 文本检测模型，切换模型需要同时修改 modelEnum 及 modelPath
-        config.setModelEnum(CommonDetModelEnum.PP_OCR_V4_SERVER_DET_MODEL);
-        // 下载模型并替换本地路径
-        config.setDetModelPath(DET_MODEL_PATH);
-        config.setDevice(device);
-        return OcrModelFactory.getInstance().getDetModel(config);
-    }
-
-    /**
-     * 获取方向检测模型
-     * @return
-     */
-    public OcrDirectionModel getDirectionModel() {
-        DirectionModelConfig directionModelConfig = new DirectionModelConfig();
-        // 行文本方向检测模型，切换模型需要同时修改 modelEnum 及 modelPath
-        directionModelConfig.setModelEnum(DirectionModelEnum.PP_LCNET_X0_25);
-        // 下载模型并替换本地路径
-        directionModelConfig.setModelPath(DIRECTION_MODEL_PATH);
-        directionModelConfig.setTextDetModel(getDetectionModel());
-        directionModelConfig.setDevice(device);
-        return OcrModelFactory.getInstance().getDirectionModel(directionModelConfig);
-    }
-
-    /**
-     * 获取 OCR 识别模型
-     * @return
-     */
-    public OcrCommonRecModel getRecModel() {
-        OcrRecModelConfig recModelConfig = new OcrRecModelConfig();
-        // 文本识别模型，切换模型需要同时修改 modelEnum 及 modelPath
-        recModelConfig.setRecModelEnum(CommonRecModelEnum.PP_OCR_V5_MOBILE_REC_MODEL);
-        // 下载模型并替换本地路径
-        recModelConfig.setRecModelPath(REC_MODEL_PATH);
-        recModelConfig.setTextDetModel(getDetectionModel());
-        recModelConfig.setDevice(device);
-        return OcrModelFactory.getInstance().getRecModel(recModelConfig);
-    }
-
-    /**
-     * 创建身份证识别器
-     * @return
-     */
-    public DefaultIdCardRecognizer getIdCardRecognizer() {
-        return new DefaultIdCardRecognizer()
-                .setRecModel(getRecModel())
-                // 身份证识别建议按行返回，方向矫正交给预处理逻辑处理
-                .setRecOptions(new OcrRecOptions(false, true))
-                .setDirectionModel(getDirectionModel())
-                .setEnablePreprocess(true);
-    }
-
-    /**
-     * 身份证正面识别
-     * 可识别：姓名、性别、民族、出生日期、住址、身份证号
-     */
-    @Test
-    public void recognizeFront() {
-        try {
-            DefaultIdCardRecognizer recognizer = getIdCardRecognizer();
-            Image image = SmartImageFactory.getInstance().fromFile(FRONT_IMAGE_PATH);
-            IdCardFrontInfo result = recognizer.recognizeFront(image);
-            log.info("身份证正面识别结果：{}", JsonUtils.toJson(result));
-        } catch (Exception e) {
-            log.error("身份证正面识别失败", e);
-        }
-    }
-
-    /**
-     * 身份证反面识别
-     * 可识别：签发机关、有效期开始时间、有效期结束时间
-     */
-    @Test
-    public void recognizeBack() {
-        try {
-            DefaultIdCardRecognizer recognizer = getIdCardRecognizer();
-            Image image = SmartImageFactory.getInstance().fromFile(BACK_IMAGE_PATH);
-            IdCardBackInfo result = recognizer.recognizeBack(image);
-            log.info("身份证反面识别结果：{}", JsonUtils.toJson(result));
-        } catch (Exception e) {
-            log.error("身份证反面识别失败", e);
-        }
-    }
-
-    /**
-     * 同时识别身份证正反面
-     */
-    @Test
-    public void recognizeBoth() {
-        try {
-            DefaultIdCardRecognizer recognizer = getIdCardRecognizer();
-            Image frontImage = SmartImageFactory.getInstance().fromFile(FRONT_IMAGE_PATH);
-            Image backImage = SmartImageFactory.getInstance().fromFile(BACK_IMAGE_PATH);
-            IdCardInfo result = recognizer.recognizeBoth(frontImage, backImage);
-            log.info("身份证正反面识别结果：{}", JsonUtils.toJson(result));
-        } catch (Exception e) {
-            log.error("身份证正反面识别失败", e);
-        }
-    }
-
-    /**
-     * 身份证识别并输出调试图片
-     * 适合排查“为什么没识别出来”，不适合生产环境使用
-     */
-    @Test
-    public void recognizeFrontWithDebugImages() {
-        try {
-            Path debugDir = Paths.get("output/idcard_debug");
-            Files.createDirectories(debugDir);
-            String filePrefix = "idcard_front";
-            DefaultIdCardRecognizer recognizer = getIdCardRecognizer()
-//                    .setPreprocessListener(createDebugListener(debugDir, "idcard_front"));
-                    .setPreprocessListener(new IdCardPreprocessListener() {
-                @Override
-                public void onAfterDirection(Image image) {
-                    saveDebugImage(image, debugDir.resolve(filePrefix + "_step1_direction.png"));
-                }
-
-                @Override
-                public void onAfterRecognize(Image image, OcrInfo ocrInfo) {
-                    saveDebugImage(image, debugDir.resolve(filePrefix + "_step2_ocr_boxes.png"));
-                }
-            });
-
-            Image image = SmartImageFactory.getInstance().fromFile(FRONT_IMAGE_PATH);
-            IdCardFrontInfo result = recognizer.recognizeFront(image);
-            log.info("身份证正面识别结果：{}", JsonUtils.toJson(result));
-            log.info("调试图片已输出到：{}", debugDir.toAbsolutePath());
-        } catch (Exception e) {
-            log.error("身份证调试识别失败", e);
-        }
-    }
-
-    private void saveDebugImage(Image image, Path outputPath) {
-        try {
-            ImageUtils.save(image, outputPath, "png");
-        } catch (IOException e) {
-            throw new RuntimeException(e);
-        }
-    }
-}

examples/ocr-examples/src/main/java/smartai/examples/ocr/plate/PlateRecDemo.java

@@ -29,7 +29,7 @@ import java.util.List;
 /**
  * 车牌识别demo
  * 模型下载地址：https://pan.baidu.com/s/1YEP56UqYcL-Op80M6JAreA?pwd=1234 提取码: 1234
- * 开发文档：http://doc.numberone.ink/
+ * 开发文档：http://doc.smartjavaai.cn/
  * @author dwj
  */
 @Slf4j

examples/ocr-examples/src/main/java/smartai/examples/ocr/table/TableRecDemo.java

@@ -39,7 +39,7 @@ import java.util.List;
 /**
  * OCR 表格识别 示例
  * 模型下载地址：https://pan.baidu.com/s/1MLfd73Vjdpnuls9-oqc9uw?pwd=1234 提取码: 1234
- * 开发文档：http://doc.numberone.ink/
+ * 开发文档：http://doc.smartjavaai.cn/
  * @author dwj
  */
 @Slf4j

examples/speech-examples/README.md

@@ -33,6 +33,6 @@ src
 ## 📄 文档
 
 有关完整使用说明，请查阅 SmartJavaAI 官方文档：
-[http://doc.numberone.ink](http://doc.numberone.ink)
+[http://doc.smartjavaai.cn](http://doc.smartjavaai.cn)
 
 ---

examples/speech-examples/pom.xml

@@ -12,7 +12,7 @@
         <maven.compiler.source>11</maven.compiler.source>
         <maven.compiler.target>11</maven.compiler.target>
         <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
-        <smartjavaai.version>1.1.2</smartjavaai.version>
+        <smartjavaai.version>1.1.1</smartjavaai.version>
         <!--如果打包运行，需要替换成你的main-->
         <exec.mainClass>smartai.examples.speech.asr.common.OcrRecognizeDemo</exec.mainClass>
 

examples/speech-examples/src/main/java/smartai/examples/speech/asr/SpeechRecognizeDemo.java

@@ -35,7 +35,7 @@ import java.nio.file.Paths;
 /**
  * 语音识别demo（Vosk、Whisper）
  * 模型下载网盘：https://pan.baidu.com/s/1kiMF5MF641R7LTn1GpB2lQ?pwd=1234 提取码: 1234
- * 文档地址：http://doc.numberone.ink/
+ * 文档地址：http://doc.smartjavaai.cn/
  * @author dwj
  */
 @Slf4j

examples/translation-example/README.md

@@ -40,6 +40,6 @@
 ## 📄 文档
 
 有关完整使用说明，请查阅 SmartJavaAI 官方文档：
-[http://doc.numberone.ink](http://doc.numberone.ink)
+[http://doc.smartjavaai.cn](http://doc.smartjavaai.cn)
 
 ---

examples/translation-example/pom.xml

@@ -12,7 +12,7 @@
         <maven.compiler.source>11</maven.compiler.source>
         <maven.compiler.target>11</maven.compiler.target>
         <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
-        <smartjavaai.version>1.1.2</smartjavaai.version>
+        <smartjavaai.version>1.1.1</smartjavaai.version>
         <!--如果打包运行，需要替换成你的main-->
         <exec.mainClass>smartai.examples.nlp.translation.TranslationDemo</exec.mainClass>
 

examples/translation-example/src/main/java/smartai/examples/nlp/translation/TranslationDemo.java

@@ -20,7 +20,7 @@ import java.io.IOException;
 /**
  * 机器翻译Demo
  * 模型下载地址：https://pan.baidu.com/s/1wf7btnb4cyBFv7DB7baHnw?pwd=1234 提取码: 1234
- * 开发文档：http://doc.numberone.ink/
+ * 开发文档：http://doc.smartjavaai.cn/
  * @author dwj
  */
 @Slf4j

examples/vision-example/README.md

@@ -96,6 +96,6 @@ vision-example/
 ## 📄 文档
 
 有关完整使用说明，请查阅 SmartJavaAI 官方文档：
-[http://doc.numberone.ink](http://doc.numberone.ink)
+[http://doc.smartjavaai.cn](http://doc.smartjavaai.cn)
 
 ---

examples/vision-example/pom.xml

@@ -12,7 +12,7 @@
         <maven.compiler.source>11</maven.compiler.source>
         <maven.compiler.target>11</maven.compiler.target>
         <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
-        <smartjavaai.version>1.1.2</smartjavaai.version>
+        <smartjavaai.version>1.1.1</smartjavaai.version>
         <!--如果打包运行，需要替换成你的main-->
         <exec.mainClass>smartai.examples.vision.ObjectDetectionDemo</exec.mainClass>
 

examples/vision-example/src/main/java/smartai/examples/vision/ActionRecognizeDemo.java

@@ -21,7 +21,7 @@ import java.util.Arrays;
 /**
  * 动作识别Demo
  * 模型下载地址：https://pan.baidu.com/s/17doY4pgZM9EbtSIaoCWWCA?pwd=1234 提取码: 1234
- * 文档地址：http://doc.numberone.ink/
+ * 文档地址：http://doc.smartjavaai.cn/
  * @author dwj
  */
 @Slf4j
@@ -41,7 +41,7 @@ public class ActionRecognizeDemo {
     /**
      * 获取动作识别模型
      * 注意事项：
-     * 1、不同模型支持的动作类别不同，请查看文档：http://doc.numberone.ink
+     * 1、不同模型支持的动作类别不同，请查看文档：http://doc.smartjavaai.cn
      */
     public ActionRecModel getModel(){
         ActionRecModelConfig config = new ActionRecModelConfig();
@@ -62,7 +62,7 @@ public class ActionRecognizeDemo {
     /**
      * 动作识别
      * 注意事项：
-     * 1、不同模型支持的动作类别不同，请查看文档：http://doc.numberone.ink
+     * 1、不同模型支持的动作类别不同，请查看文档：http://doc.smartjavaai.cn
      * 2、图片中应该只包含单一动作人物
      * 3、动作识别，只做图片分类，并不做人物定位
      */

examples/vision-example/src/main/java/smartai/examples/vision/InstanceSegDemo.java

@@ -28,7 +28,7 @@ import java.nio.file.Paths;
 /**
  * 实例分割 Demo
  * 模型下载地址：https://pan.baidu.com/s/12nRRY9JFNDwLeg63jfBerA?pwd=1234 提取码: 1234
- * 文档地址：http://doc.numberone.ink/
+ * 文档地址：http://doc.smartjavaai.cn/
  * @author dwj
  */
 @Slf4j
@@ -50,7 +50,7 @@ public class InstanceSegDemo {
     /**
      * 获取实例分割模型
      * 注意事项：
-     * 1、更多模型请查看文档：http://doc.numberone.ink
+     * 1、更多模型请查看文档：http://doc.smartjavaai.cn
      */
     public InstanceSegModel getModel(){
         InstanceSegModelConfig config = new InstanceSegModelConfig();

examples/vision-example/src/main/java/smartai/examples/vision/ObbDetDemo.java

@@ -25,7 +25,7 @@ import java.util.Arrays;
 /**
  * obb旋转框检测demo
  * 模型下载地址：https://pan.baidu.com/s/1-tC0u-aha3tnMQwy8FKy1Q?pwd=1234 提取码: 1234
- * 文档地址：http://doc.numberone.ink/
+ * 文档地址：http://doc.smartjavaai.cn/
  * @author dwj
  */
 @Slf4j
@@ -45,7 +45,7 @@ public class ObbDetDemo {
     /**
      * 获取旋转框检测模型
      * 注意事项：
-     * 1、更多模型请查看文档：http://doc.numberone.ink
+     * 1、更多模型请查看文档：http://doc.smartjavaai.cn
      * 2、模型可检测物体请查看：模型同目录文件synset.txt
      */
     public ObbDetModel getModel(){

examples/vision-example/src/main/java/smartai/examples/vision/ObjectDetectionDemo.java

@@ -45,7 +45,7 @@ import java.util.concurrent.CountDownLatch;
 /**
  * 目标检测模型demo
  * 模型下载地址：https://pan.baidu.com/s/10aTOLBlR6EG-sq6g0OkAWg?pwd=1234 提取码: 1234
- * 文档地址：http://doc.numberone.ink/
+ * 文档地址：http://doc.smartjavaai.cn/
  * @author dwj
  */
 @Slf4j
@@ -66,7 +66,7 @@ public class ObjectDetectionDemo {
     /**
      * 获取目标检测模型
      * 注意事项：
-     * 1、更多模型请查看文档：http://doc.numberone.ink/objectdetect.html
+     * 1、更多模型请查看文档：http://doc.smartjavaai.cn/objectdetect.html
      */
     public DetectorModel getModel(){
         DetectorModelConfig config = new DetectorModelConfig();
@@ -147,7 +147,7 @@ public class ObjectDetectionDemo {
             DetectorModelConfig config = new DetectorModelConfig();
             //目标检测模型，切换模型需要同时修改modelEnum及modelPath
             config.setModelEnum(DetectorModelEnum.YOLOV12_CUSTOM_ONNX);
-            //模型所在路径，synset.txt也需要放在同目录下(分类文件，具体请看文档：http://doc.numberone.ink/objectdetect.html#%E4%BD%BF%E7%94%A8%E8%87%AA%E5%B7%B1%E8%AE%AD%E7%BB%83%E7%9A%84%E6%A8%A1%E5%9E%8B%E6%A3%80%E6%B5%8B)
+            //模型所在路径，synset.txt也需要放在同目录下(分类文件，具体请看文档：http://doc.smartjavaai.cn/objectdetect.html#%E4%BD%BF%E7%94%A8%E8%87%AA%E5%B7%B1%E8%AE%AD%E7%BB%83%E7%9A%84%E6%A8%A1%E5%9E%8B%E6%A3%80%E6%B5%8B)
             config.setModelPath("/Users/xxx/Documents/develop/fire_model/best.onnx");
             //模型训练时图片宽度
             config.putCustomParam("width", 640);//resize 宽

examples/vision-example/src/main/java/smartai/examples/vision/PersonDetectDemo.java

@@ -21,7 +21,7 @@ import java.nio.file.Paths;
 /**
  * 行人检测案例
  * 模型下载地址：https://pan.baidu.com/s/1EWfExw7pYjKEH5uR5wf3Rw?pwd=1234 提取码: 1234
- * 文档地址：http://doc.numberone.ink/
+ * 文档地址：http://doc.smartjavaai.cn/
  * @author dwj
  */
 @Slf4j

examples/vision-example/src/main/java/smartai/examples/vision/PoseDetDemo.java

@@ -22,7 +22,7 @@ import java.nio.file.Paths;
 /**
  * 姿态估计demo
  * 模型下载地址：https://pan.baidu.com/s/1pPYyl1V2CpcMYCO8CJQHGg?pwd=1234 提取码: 1234
- * 文档地址：http://doc.numberone.ink/
+ * 文档地址：http://doc.smartjavaai.cn/
  * @author dwj
  */
 @Slf4j
@@ -42,7 +42,7 @@ public class PoseDetDemo {
     /**
      * 获取姿态估计模型
      * 注意事项：
-     * 1、更多模型请查看文档：http://doc.numberone.ink
+     * 1、更多模型请查看文档：http://doc.smartjavaai.cn
      */
     public PoseModel getModel(){
         PoseModelConfig config = new PoseModelConfig();

examples/vision-example/src/main/java/smartai/examples/vision/SemSegDemo.java

@@ -23,7 +23,7 @@ import java.util.Arrays;
 /**
  * 语义分割 Demo 通过网盘分享的文件：语义分割（semantic_segmentation）
  * 模型下载地址：https://pan.baidu.com/s/18gs9E5h_d9imPmNLHuDo9A?pwd=1234 提取码: 1234
- * 文档地址：http://doc.numberone.ink/
+ * 文档地址：http://doc.smartjavaai.cn/
  * @author dwj
  */
 @Slf4j
@@ -45,7 +45,7 @@ public class SemSegDemo {
     /**
      * 获取语义分割模型
      * 注意事项：
-     * 1、更多模型请查看文档：http://doc.numberone.ink
+     * 1、更多模型请查看文档：http://doc.smartjavaai.cn
      */
     public SemSegModel getModel(){
         SemSegModelConfig config = new SemSegModelConfig();

face/pom.xml

@@ -6,11 +6,11 @@
     <parent>
         <groupId>cn.smartjavaai</groupId>
         <artifactId>smartjavaai-parent</artifactId>
-        <version>1.1.2</version>
+        <version>1.1.1</version>
     </parent>
 
     <artifactId>face</artifactId>
-    <version>1.1.2</version>
+    <version>1.1.1</version>
     <name>face</name>
     <description>SmartJavaAI</description>
     <url>https://github.com/geekwenjie/SmartJavaAI</url>

ocr/pom.xml

@@ -6,7 +6,7 @@
     <parent>
         <groupId>cn.smartjavaai</groupId>
         <artifactId>smartjavaai-parent</artifactId>
-        <version>1.1.2</version>
+        <version>1.1.1</version>
     </parent>
 
     <artifactId>ocr</artifactId>
@@ -42,7 +42,7 @@
         </dependency>
     </dependencies>
 
-    <version>1.1.2</version>
+    <version>1.1.1</version>
     <name>ocr</name>
     <description>SmartJavaAI</description>
     <url>https://github.com/geekwenjie/SmartJavaAI</url>

ocr/src/main/java/cn/smartjavaai/ocr/config/OcrRecOptions.java

@@ -21,7 +21,6 @@ public class OcrRecOptions {
     private boolean enableLineSplit = true;
 
 
-
     public OcrRecOptions(boolean enableDirectionCorrect, boolean enableLineSplit) {
         this.enableDirectionCorrect = enableDirectionCorrect;
         this.enableLineSplit = enableLineSplit;

ocr/src/main/java/cn/smartjavaai/ocr/entity/IdCardBackInfo.java

@@ -1,26 +0,0 @@
-package cn.smartjavaai.ocr.entity;
-
-import lombok.Data;
-
-/**
- * 身份证反面信息
- */
-@Data
-public class IdCardBackInfo {
-
-    /**
-     * 签发机关
-     */
-    private String issuingAuthority;
-
-    /**
-     * 有效期开始日期，格式：yyyy-MM-dd
-     */
-    private String validFrom;
-
-    /**
-     * 有效期结束日期，格式：yyyy-MM-dd 或 "长期"
-     */
-    private String validTo;
-}
-

ocr/src/main/java/cn/smartjavaai/ocr/entity/IdCardFrontInfo.java

@@ -1,41 +0,0 @@
-package cn.smartjavaai.ocr.entity;
-
-import lombok.Data;
-
-/**
- * 身份证正面信息
- */
-@Data
-public class IdCardFrontInfo {
-
-    /**
-     * 姓名
-     */
-    private String name;
-
-    /**
-     * 性别（男/女）
-     */
-    private String gender;
-
-    /**
-     * 民族（如：汉、满、回等，不带“族”字）
-     */
-    private String ethnicity;
-
-    /**
-     * 公民身份号码
-     */
-    private String idNumber;
-
-    /**
-     * 出生日期，格式：yyyy-MM-dd
-     */
-    private String birthday;
-
-    /**
-     * 住址
-     */
-    private String address;
-}
-

ocr/src/main/java/cn/smartjavaai/ocr/entity/IdCardInfo.java

@@ -1,26 +1,12 @@
 package cn.smartjavaai.ocr.entity;
 
-import lombok.Data;
-
 /**
- * 身份证信息（聚合正反面）
+ * 身份证信息
- *
- * 当前主要使用正面信息，预留反面字段，方便后续扩展。
- *
  * @author dwj
  * @date 2025/5/22
  */
-@Data
 public class IdCardInfo {
 
-    /**
-     * 身份证正面信息
-     */
-    private IdCardFrontInfo front;
 
-    /**
+
-     * 身份证反面信息
-     */
-    private IdCardBackInfo back;
 }
-

ocr/src/main/java/cn/smartjavaai/ocr/idcard/DefaultIdCardRecognizer.java

@@ -1,203 +0,0 @@
-package cn.smartjavaai.ocr.idcard;
-
-import ai.djl.modality.cv.Image;
-import ai.djl.util.JsonUtils;
-import cn.smartjavaai.common.utils.ImageUtils;
-import cn.smartjavaai.ocr.config.OcrRecOptions;
-import cn.smartjavaai.ocr.entity.IdCardBackInfo;
-import cn.smartjavaai.ocr.entity.IdCardFrontInfo;
-import cn.smartjavaai.ocr.entity.IdCardInfo;
-import cn.smartjavaai.ocr.entity.OcrItem;
-import cn.smartjavaai.ocr.entity.OcrInfo;
-import cn.smartjavaai.ocr.model.common.direction.OcrDirectionModel;
-import cn.smartjavaai.ocr.model.common.recognize.OcrCommonRecModel;
-import cn.smartjavaai.ocr.utils.OcrUtils;
-import lombok.Data;
-import lombok.extern.slf4j.Slf4j;
-import lombok.experimental.Accessors;
-
-import java.util.List;
-
-/**
- * 身份证 OCR 识别服务实现
- *
- * - 支持从 Image 开始：内部完成预处理（方向矫正 OcrDirectionModel）+ OCR + 解析
- * - 支持从 OcrInfo 开始：只做结构化解析
- *
- * 通过 Lombok @Accessors(chain = true) 支持链式设置依赖。
- */
-@Slf4j
-@Data
-@Accessors(chain = true)
-public class DefaultIdCardRecognizer implements IdCardRecognizer {
-
-    private static final int DEBUG_DRAW_FONT_SIZE = 20;
-
-    /**
-     * 文本识别模型（可选；仅当从 Image 开始时需要）
-     */
-    private OcrCommonRecModel recModel;
-
-    /**
-     * 识别选项（如是否方向矫正、是否按行返回等）
-     */
-    private OcrRecOptions recOptions;
-
-    /**
-     * 方向检测模型（可选；预处理时用于 0/90/180/270° 整图方向矫正）。
-     * 未设置则跳过方向矫正。
-     */
-    private OcrDirectionModel directionModel;
-
-    /**
-     * 图像预处理器（方向矫正，可选，默认按需 lazy-init）
-     */
-    private IdCardPreprocessor preprocessor;
-
-    /**
-     * 正面解析器（默认按需 lazy-init 为 IdCardFrontParser）
-     */
-    private IdCardParser<IdCardFrontInfo> frontParser;
-
-    /**
-     * 反面解析器（默认按需 lazy-init 为 IdCardBackParser）
-     */
-    private IdCardParser<IdCardBackInfo> backParser;
-
-    /**
-     * 校验器（可选，默认按需 lazy-init 为 IdCardValidator）
-     */
-    private IdCardValidator validator;
-
-    /**
-     * 是否进行图像预处理（方向矫正）。默认 true。
-     * 若图片已校正，可设为 false 以跳过预处理、节省时间。
-     */
-    private boolean enablePreprocess = true;
-
-    /**
-     * 预处理调试监听器（可选）。
-     * 若设置，则在方向矫正阶段结束后回调当前图像，
-     * 方便最终用户在需要时保存中间结果进行排查和可视化调试。
-     * 默认 null，不影响正常业务逻辑。
-     */
-    private IdCardPreprocessListener preprocessListener;
-
-    @Override
-    public IdCardFrontInfo recognizeFront(Image image) {
-        if (recModel == null) {
-            throw new IllegalStateException("recModel 未配置，无法从 Image 执行身份证识别，请先设置 recModel 或改用 recognizeFront(OcrInfo)。");
-        }
-        long totalStart = System.nanoTime();
-        Image toRecognize = image;
-        IdCardPreprocessResult preprocessResult = null;
-        if (enablePreprocess) {
-            long preprocessStart = System.nanoTime();
-            IdCardPreprocessor pp = (preprocessor != null) ? preprocessor : (preprocessor = new IdCardPreprocessor());
-            preprocessResult = pp.preprocess(image, recModel.getTextDetModel(), directionModel, preprocessListener);
-            toRecognize = preprocessResult.getProcessedImage();
-            log.debug("身份证正面预处理耗时={}ms", elapsedMillis(preprocessStart));
-        }
-        long recognizeStart = System.nanoTime();
-        OcrInfo ocrInfo = preprocessResult != null
-                && !preprocessResult.isRotated()
-                && preprocessResult.getReusableBoxes() != null
-                && !preprocessResult.getReusableBoxes().isEmpty()
-                ? recModel.recognize(toRecognize, preprocessResult.getReusableBoxes(), recOptions)
-                : recModel.recognize(toRecognize, recOptions);
-        log.debug("身份证正面OCR模型调用耗时={}ms", elapsedMillis(recognizeStart));
-        notifyAfterRecognize(toRecognize, ocrInfo);
-        IdCardFrontInfo info = recognizeFront(ocrInfo);
-        log.debug("身份证正面总耗时={}ms", elapsedMillis(totalStart));
-        return info;
-    }
-
-    @Override
-    public IdCardBackInfo recognizeBack(Image image) {
-        if (recModel == null) {
-            throw new IllegalStateException("recModel 未配置，无法从 Image 执行身份证识别，请先设置 recModel 或改用 recognizeBack(OcrInfo)。");
-        }
-        long totalStart = System.nanoTime();
-        Image toRecognize = image;
-        IdCardPreprocessResult preprocessResult = null;
-        if (enablePreprocess) {
-            long preprocessStart = System.nanoTime();
-            IdCardPreprocessor pp = (preprocessor != null) ? preprocessor : (preprocessor = new IdCardPreprocessor());
-            preprocessResult = pp.preprocess(image, recModel.getTextDetModel(), directionModel, preprocessListener);
-            toRecognize = preprocessResult.getProcessedImage();
-            log.debug("身份证反面预处理耗时={}ms", elapsedMillis(preprocessStart));
-        }
-        long recognizeStart = System.nanoTime();
-        OcrInfo ocrInfo = preprocessResult != null
-                && !preprocessResult.isRotated()
-                && preprocessResult.getReusableBoxes() != null
-                && !preprocessResult.getReusableBoxes().isEmpty()
-                ? recModel.recognize(toRecognize, preprocessResult.getReusableBoxes(), recOptions)
-                : recModel.recognize(toRecognize, recOptions);
-        log.debug("身份证反面OCR模型调用耗时={}ms", elapsedMillis(recognizeStart));
-        notifyAfterRecognize(toRecognize, ocrInfo);
-        IdCardBackInfo info = recognizeBack(ocrInfo);
-        log.debug("身份证反面总耗时={}ms", elapsedMillis(totalStart));
-        return info;
-    }
-
-    private void notifyAfterRecognize(Image toRecognize, OcrInfo ocrInfo) {
-        if (preprocessListener == null || toRecognize == null || ocrInfo == null) {
-            return;
-        }
-        try {
-            //打印
-            log.debug("身份证 OCR 调试结果：{}", JsonUtils.toJson(ocrInfo));
-            Image drawImage = ImageUtils.copy(toRecognize);
-            List<OcrItem> ocrItems = ocrInfo.getOcrItemList();
-            if ((ocrItems == null || ocrItems.isEmpty()) && ocrInfo.getLineList() != null) {
-                ocrItems = ocrInfo.flattenLines();
-            }
-            if (ocrItems != null && !ocrItems.isEmpty()) {
-                OcrUtils.drawOcrResult(drawImage, ocrItems, DEBUG_DRAW_FONT_SIZE);
-            }
-            preprocessListener.onAfterRecognize(drawImage, ocrInfo);
-        } catch (Exception e) {
-            log.warn("身份证 OCR 调试结果绘制失败，跳过 onAfterRecognize 回调。", e);
-        }
-    }
-
-
-    @Override
-    public IdCardFrontInfo recognizeFront(OcrInfo ocrInfo) {
-        long start = System.nanoTime();
-        IdCardParser<IdCardFrontInfo> parser =
-                (frontParser != null) ? frontParser : (frontParser = new IdCardFrontParser());
-        IdCardFrontInfo info = parser.parse(ocrInfo);
-        IdCardValidator v = (validator != null) ? validator : (validator = new IdCardValidator());
-        v.validateFront(info);
-        return info;
-    }
-
-    @Override
-    public IdCardBackInfo recognizeBack(OcrInfo ocrInfo) {
-        IdCardParser<IdCardBackInfo> parser =
-                (backParser != null) ? backParser : (backParser = new IdCardBackParser());
-        IdCardBackInfo info = parser.parse(ocrInfo);
-        IdCardValidator v = (validator != null) ? validator : (validator = new IdCardValidator());
-        v.validateBack(info);
-        return info;
-    }
-
-    @Override
-    public IdCardInfo recognizeBoth(Image frontImage, Image backImage) {
-        long totalStart = System.nanoTime();
-        IdCardInfo info = new IdCardInfo();
-        info.setFront(recognizeFront(frontImage));
-        info.setBack(recognizeBack(backImage));
-        IdCardValidator v = (validator != null) ? validator : (validator = new IdCardValidator());
-        v.validate(info);
-        log.debug("身份证正反面识别总耗时={}ms", elapsedMillis(totalStart));
-        return info;
-    }
-
-    private long elapsedMillis(long startNanos) {
-        return (System.nanoTime() - startNanos) / 1_000_000;
-    }
-
-}

ocr/src/main/java/cn/smartjavaai/ocr/idcard/IdCardBackParser.java

@@ -1,335 +0,0 @@
-package cn.smartjavaai.ocr.idcard;
-
-import cn.smartjavaai.ocr.entity.IdCardBackInfo;
-import cn.smartjavaai.ocr.entity.OcrBox;
-import cn.smartjavaai.ocr.entity.OcrInfo;
-import cn.smartjavaai.ocr.entity.OcrItem;
-import cn.smartjavaai.ocr.utils.BoxUtils;
-
-import java.util.ArrayList;
-import java.util.Arrays;
-import java.util.IdentityHashMap;
-import java.util.List;
-import java.util.Map;
-import java.util.Objects;
-import java.util.regex.Matcher;
-import java.util.regex.Pattern;
-import java.util.stream.Collectors;
-
-/**
- * 身份证反面解析逻辑。
- *
- * 解析：
- * - 签发机关
- * - 有效期限（起止日期 / 长期）
- */
-public class IdCardBackParser implements IdCardParser<IdCardBackInfo> {
-
-    private static final Pattern VALID_PERIOD_PATTERN = Pattern.compile(
-            "((?:19|20)\\d{2})[./-]?((?:1[0-2])|(?:0[1-9]))[./-]?((?:3[01])|(?:[12]\\d)|(?:0[1-9]))" +
-                    "\\s*[-一至到~]\\s*" +
-                    "(((?:19|20)\\d{2})[./-]?((?:1[0-2])|(?:0[1-9]))[./-]?((?:3[01])|(?:[12]\\d)|(?:0[1-9]))|长期)"
-    );
-
-    @Override
-    public IdCardBackInfo parse(OcrInfo ocrInfo) {
-        IdCardBackInfo info = new IdCardBackInfo();
-        if (ocrInfo == null || ocrInfo.getLineList() == null || ocrInfo.getLineList().isEmpty()) {
-            return info;
-        }
-
-        List<OcrItem> allItems = ocrInfo.getLineList().stream()
-                .filter(Objects::nonNull)
-                .flatMap(List::stream)
-                .filter(Objects::nonNull)
-                .collect(Collectors.toList());
-
-        allItems = filterSmallBoxes(allItems);
-
-        if (allItems.isEmpty()) {
-            return info;
-        }
-        Map<OcrBox, OcrItem> itemByBox = buildBoxItemMap(allItems);
-
-        OcrItem authorityLabel = findLabel(allItems, Arrays.asList("签发机关", "签发機关", "签发机関", "签发"));
-        if (authorityLabel != null) {
-            String authority = findIssuingAuthority(authorityLabel, allItems, itemByBox);
-            info.setIssuingAuthority(authority);
-        }
-
-        OcrItem validLabel = findLabel(allItems, Arrays.asList("有效期限", "有效期", "有效期限:", "有效期限："));
-        if (validLabel != null) {
-            ValidPeriod validPeriod = findValidPeriod(validLabel, allItems, itemByBox);
-            if (validPeriod != null) {
-                info.setValidFrom(validPeriod.validFrom);
-                info.setValidTo(validPeriod.validTo);
-            }
-        }
-
-        if (info.getIssuingAuthority() == null || info.getIssuingAuthority().isEmpty()) {
-            info.setIssuingAuthority(findIssuingAuthorityByGlobalFallback(allItems));
-        }
-        if (info.getValidFrom() == null || info.getValidTo() == null) {
-            ValidPeriod fallback = findValidPeriodByGlobalFallback(allItems);
-            if (fallback != null) {
-                if (info.getValidFrom() == null) {
-                    info.setValidFrom(fallback.validFrom);
-                }
-                if (info.getValidTo() == null) {
-                    info.setValidTo(fallback.validTo);
-                }
-            }
-        }
-
-        return info;
-    }
-
-    private String findIssuingAuthority(OcrItem authorityLabel, List<OcrItem> allItems, Map<OcrBox, OcrItem> itemByBox) {
-        String selfAuthority = extractIssuingAuthority(authorityLabel.getText());
-        if (selfAuthority != null) {
-            return selfAuthority;
-        }
-
-        List<OcrBox> boxList = allItems.stream()
-                .map(OcrItem::getOcrBox)
-                .filter(Objects::nonNull)
-                .collect(Collectors.toList());
-
-        OcrBox anchorBox = authorityLabel.getOcrBox();
-        String authority = findAuthorityByDirection(anchorBox, itemByBox, boxList, BoxUtils.Direction.RIGHT, 3);
-        if (authority != null) {
-            return authority;
-        }
-        authority = findAuthorityByDirection(anchorBox, itemByBox, boxList, BoxUtils.Direction.DOWN, 3);
-        if (authority != null) {
-            return authority;
-        }
-        authority = findAuthorityByDirection(anchorBox, itemByBox, boxList, BoxUtils.Direction.UP, 2);
-        if (authority != null) {
-            return authority;
-        }
-        return null;
-    }
-
-    private String findAuthorityByDirection(OcrBox anchorBox,
-                                            Map<OcrBox, OcrItem> itemByBox,
-                                            List<OcrBox> boxList,
-                                            BoxUtils.Direction direction,
-                                            int limit) {
-        List<OcrBox> neighbors = BoxUtils.findNearestBoxes(anchorBox, boxList, direction, limit);
-        if (neighbors == null || neighbors.isEmpty()) {
-            return null;
-        }
-
-        List<OcrItem> neighborItems = neighbors.stream()
-                .map(itemByBox::get)
-                .filter(Objects::nonNull)
-                .collect(Collectors.toList());
-
-        for (OcrItem item : neighborItems) {
-            String authority = extractIssuingAuthority(item.getText());
-            if (authority != null) {
-                return authority;
-            }
-        }
-
-        String merged = neighborItems.stream()
-                .map(OcrItem::getText)
-                .filter(Objects::nonNull)
-                .map(this::normalizeText)
-                .collect(Collectors.joining());
-        return extractIssuingAuthority(merged);
-    }
-
-    private String extractIssuingAuthority(String text) {
-        if (text == null || text.isEmpty()) {
-            return null;
-        }
-        String clean = normalizeText(text)
-                .replace("签发机关", "")
-                .replace("签发機关", "")
-                .replace("签发机関", "")
-                .replace("签发", "");
-        if (clean.isEmpty()) {
-            return null;
-        }
-        if (clean.contains("有效期限") || clean.contains("有效期")) {
-            return null;
-        }
-        return clean;
-    }
-
-    private ValidPeriod findValidPeriod(OcrItem validLabel, List<OcrItem> allItems, Map<OcrBox, OcrItem> itemByBox) {
-        ValidPeriod selfPeriod = extractValidPeriod(validLabel.getText());
-        if (selfPeriod != null) {
-            return selfPeriod;
-        }
-
-        List<OcrBox> boxList = allItems.stream()
-                .map(OcrItem::getOcrBox)
-                .filter(Objects::nonNull)
-                .collect(Collectors.toList());
-
-        OcrBox anchorBox = validLabel.getOcrBox();
-        ValidPeriod period = findValidPeriodByDirection(anchorBox, itemByBox, boxList, BoxUtils.Direction.RIGHT, 4);
-        if (period != null) {
-            return period;
-        }
-        period = findValidPeriodByDirection(anchorBox, itemByBox, boxList, BoxUtils.Direction.DOWN, 4);
-        if (period != null) {
-            return period;
-        }
-        period = findValidPeriodByDirection(anchorBox, itemByBox, boxList, BoxUtils.Direction.UP, 2);
-        if (period != null) {
-            return period;
-        }
-        return null;
-    }
-
-    private ValidPeriod findValidPeriodByDirection(OcrBox anchorBox,
-                                                   Map<OcrBox, OcrItem> itemByBox,
-                                                   List<OcrBox> boxList,
-                                                   BoxUtils.Direction direction,
-                                                   int limit) {
-        List<OcrBox> neighbors = BoxUtils.findNearestBoxes(anchorBox, boxList, direction, limit);
-        if (neighbors == null || neighbors.isEmpty()) {
-            return null;
-        }
-
-        List<OcrItem> neighborItems = neighbors.stream()
-                .map(itemByBox::get)
-                .filter(Objects::nonNull)
-                .collect(Collectors.toList());
-
-        for (OcrItem item : neighborItems) {
-            ValidPeriod period = extractValidPeriod(item.getText());
-            if (period != null) {
-                return period;
-            }
-        }
-
-        String merged = neighborItems.stream()
-                .map(OcrItem::getText)
-                .filter(Objects::nonNull)
-                .map(this::normalizeText)
-                .collect(Collectors.joining());
-        return extractValidPeriod(merged);
-    }
-
-    private ValidPeriod extractValidPeriod(String text) {
-        if (text == null || text.isEmpty()) {
-            return null;
-        }
-        String clean = normalizeText(text)
-                .replace("有效期限", "")
-                .replace("有效期", "");
-
-        Matcher matcher = VALID_PERIOD_PATTERN.matcher(clean);
-        if (!matcher.find()) {
-            return null;
-        }
-
-        String from = normalizeDate(matcher.group(1), matcher.group(2), matcher.group(3));
-        if (from == null) {
-            return null;
-        }
-
-        String toRaw = matcher.group(4);
-        String to = "长期".equals(toRaw)
-                ? "长期"
-                : normalizeDate(matcher.group(5), matcher.group(6), matcher.group(7));
-        if (to == null) {
-            return null;
-        }
-
-        return new ValidPeriod(from, to);
-    }
-
-    private String normalizeDate(String year, String month, String day) {
-        if (year == null || month == null || day == null) {
-            return null;
-        }
-        return year + "-" + month + "-" + day;
-    }
-
-    private String findIssuingAuthorityByGlobalFallback(List<OcrItem> allItems) {
-        for (OcrItem item : allItems) {
-            String authority = extractIssuingAuthority(item.getText());
-            if (authority != null && (authority.contains("公安局") || authority.contains("分局") || authority.contains("机关"))) {
-                return authority;
-            }
-        }
-        return null;
-    }
-
-    private ValidPeriod findValidPeriodByGlobalFallback(List<OcrItem> allItems) {
-        List<String> texts = new ArrayList<>();
-        for (OcrItem item : allItems) {
-            if (item != null && item.getText() != null) {
-                ValidPeriod period = extractValidPeriod(item.getText());
-                if (period != null) {
-                    return period;
-                }
-                texts.add(normalizeText(item.getText()));
-            }
-        }
-        return extractValidPeriod(String.join("", texts));
-    }
-
-    private OcrItem findLabel(List<OcrItem> allItems, List<String> keywords) {
-        for (OcrItem item : allItems) {
-            String text = item.getText();
-            if (text == null) {
-                continue;
-            }
-            String clean = normalizeText(text);
-            for (String keyword : keywords) {
-                if (clean.contains(keyword)) {
-                    return item;
-                }
-            }
-        }
-        return null;
-    }
-
-    private String normalizeText(String text) {
-        if (text == null) {
-            return "";
-        }
-        return text.replace(" ", "")
-                .replace("一", "-")
-                .replace("到", "-")
-                .replace("至", "-")
-                .replace("~", "-")
-                .replace("—", "-")
-                .replace("－", "-");
-    }
-
-    private List<OcrItem> filterSmallBoxes(List<OcrItem> items) {
-        List<OcrItem> result = IdCardOcrUtils.filterSmallBoxes(items, 0.15);
-        return result;
-    }
-
-    private Map<OcrBox, OcrItem> buildBoxItemMap(List<OcrItem> items) {
-        Map<OcrBox, OcrItem> itemByBox = new IdentityHashMap<>();
-        if (items == null) {
-            return itemByBox;
-        }
-        for (OcrItem item : items) {
-            if (item != null && item.getOcrBox() != null) {
-                itemByBox.put(item.getOcrBox(), item);
-            }
-        }
-        return itemByBox;
-    }
-
-    private static class ValidPeriod {
-        private final String validFrom;
-        private final String validTo;
-
-        private ValidPeriod(String validFrom, String validTo) {
-            this.validFrom = validFrom;
-            this.validTo = validTo;
-        }
-    }
-}

ocr/src/main/java/cn/smartjavaai/ocr/idcard/IdCardFrontParser.java

@@ -1,930 +0,0 @@
-package cn.smartjavaai.ocr.idcard;
-
-import cn.smartjavaai.common.entity.Point;
-import cn.smartjavaai.ocr.entity.IdCardFrontInfo;
-import cn.smartjavaai.ocr.entity.OcrBox;
-import cn.smartjavaai.ocr.entity.OcrInfo;
-import cn.smartjavaai.ocr.entity.OcrItem;
-import cn.smartjavaai.ocr.utils.BoxUtils;
-import lombok.extern.slf4j.Slf4j;
-
-import java.time.DateTimeException;
-import java.time.LocalDate;
-import java.util.*;
-import java.util.function.Function;
-import java.util.regex.Matcher;
-import java.util.regex.Pattern;
-import java.util.stream.Collectors;
-
-/**
- * 身份证正面解析逻辑。
- *
- * 从 OCR 结果中解析：
- * - 姓名（兼容“姓名+值在同一检测框”与“键值分框”的情况）
- * - 性别
- * - 民族
- * - 出生日期
- * - 公民身份号码
- * - 住址
- */
-@Slf4j
-public class IdCardFrontParser implements IdCardParser<IdCardFrontInfo> {
-
-    private static final Pattern BIRTHDAY_WITH_SEPARATORS = Pattern.compile(
-            "((?:19|20)\\d{2})[年/._-]?((?:1[0-2])|(?:0?[1-9]))[月/._-]?((?:3[01])|(?:[12]\\d)|(?:0?[1-9]))日?"
-    );
-
-    private static final Pattern BIRTHDAY_COMPACT = Pattern.compile(
-            "((?:19|20)\\d{2})((?:1[0-2])|(?:0[1-9]))((?:3[01])|(?:[12]\\d)|(?:0[1-9]))"
-    );
-
-    @Override
-    public IdCardFrontInfo parse(OcrInfo ocrInfo) {
-        IdCardFrontInfo info = new IdCardFrontInfo();
-        if (ocrInfo == null || ocrInfo.getLineList() == null || ocrInfo.getLineList().isEmpty()) {
-            return info;
-        }
-
-        // 扁平化所有 item
-        List<OcrItem> allItems = ocrInfo.getLineList().stream()
-                .filter(Objects::nonNull)
-                .flatMap(List::stream)
-                .filter(Objects::nonNull)
-                .collect(Collectors.toList());
-
-        // 过滤明显过小的检测框（噪声）
-        allItems = filterSmallBoxes(allItems);
-
-        // 可用的检测框列表：解析出字段后逐步移除，减少后续遍历量
-        List<OcrItem> remainingItems = new ArrayList<>(allItems);
-        Map<OcrBox, OcrItem> itemByBox = buildBoxItemMap(remainingItems);
-
-        // 1. 姓名（兼容“标签+值同框”以及右侧单独值框）
-        OcrItem nameLabel = findLabel(remainingItems, Arrays.asList("姓名", "姓名:", "姓名："));
-        if (nameLabel != null) {
-            StringBuilder nameSb = new StringBuilder();
-            if (nameLabel.getText() != null) {
-                nameSb.append(nameLabel.getText());
-            }
-            // 右侧最近一个框（可能是姓名值）
-            OcrItem rightItem = findNearestRightItem(nameLabel, remainingItems, itemByBox);
-            if (rightItem != null && rightItem.getText() != null) {
-                nameSb.append(rightItem.getText());
-            }
-
-            // 过滤：只保留中文字符
-            String nameText = nameSb.toString()
-                    .replace(" ", "")
-                    .replaceAll("[^\\u4e00-\\u9fa5]", "");
-
-            // 提取"姓名"后面的内容作为名字
-            String name = extractNameAfterLabel(nameText);
-            if (name != null && !name.isEmpty()) {
-                info.setName(name);
-                remainingItems.remove(nameLabel);
-                if (rightItem != null) {
-                    remainingItems.remove(rightItem);
-                }
-                itemByBox = buildBoxItemMap(remainingItems);
-            }
-        }
-
-        // 2. 性别（优先右 → 上 → 下 → 全局）
-        OcrItem genderLabel = findLabel(remainingItems, Arrays.asList("性别", "性别:", "性别："));
-        if (genderLabel != null) {
-            String gender = findGenderAroundLabel(genderLabel, remainingItems, itemByBox);
-            if (gender != null) {
-                info.setGender(gender);
-                final String g = gender;
-                // 移除性别相关检测框（若同时包含民族信息则保留用于民族解析）
-                remainingItems.removeIf(it -> {
-                    String t = it.getText();
-                    if (t == null) {
-                        return false;
-                    }
-                    String clean = t.replace(" ", "");
-                    if (clean.contains("性别") && !clean.contains("民族")) {
-                        return true;
-                    }
-                    String cg = cleanGender(t);
-                    return g.equals(cg) && !clean.contains("民族");
-                });
-                itemByBox = buildBoxItemMap(remainingItems);
-            }
-        }
-
-        // 3. 民族（策略同性别）
-        OcrItem ethnicLabel = findLabel(remainingItems, Arrays.asList("民族", "民族:", "民族："));
-        if (ethnicLabel != null) {
-            String ethnicity = findEthnicityAroundLabel(ethnicLabel, remainingItems, itemByBox);
-            if (ethnicity != null) {
-                info.setEthnicity(ethnicity);
-                final String eth = ethnicity;
-                remainingItems.removeIf(it -> {
-                    String t = it.getText();
-                    if (t == null) {
-                        return false;
-                    }
-                    String clean = t.replace(" ", "");
-                    if (clean.contains("民族")) {
-                        return true;
-                    }
-                    String e = extractEthnicity(t);
-                    return eth.equals(e);
-                });
-                itemByBox = buildBoxItemMap(remainingItems);
-            }
-        }
-
-        // 4. 公民身份号码
-        OcrItem idSourceItem = null;
-        OcrItem idRightItem = null;
-        for (OcrItem item : remainingItems) {
-            String text = normalizeText(item.getText());
-            if (text.contains("公民身份号码") || text.contains("公民身份號碼") || text.toUpperCase().contains("ID")) {
-                String idInBox = extractIdNumber(text);
-                if (idInBox == null) {
-                    OcrItem idRight = findNearestRightItem(item, remainingItems, itemByBox);
-                    if (idRight != null) {
-                        idInBox = extractIdNumber(normalizeText(idRight.getText()));
-                        idRightItem = idRight;
-                    }
-                }
-                if (idInBox != null) {
-                    info.setIdNumber(idInBox);
-                    idSourceItem = item;
-                    break;
-                }
-            }
-        }
-        if (idSourceItem != null) {
-            remainingItems.remove(idSourceItem);
-        }
-        if (idRightItem != null) {
-            remainingItems.remove(idRightItem);
-        }
-        if (idSourceItem != null || idRightItem != null) {
-            itemByBox = buildBoxItemMap(remainingItems);
-        }
-
-        // 5. 出生日期：考虑多框拆分 & 与标签同框等多种情况
-        OcrItem birthLabel = findLabel(remainingItems, Arrays.asList("出生", "出生日期", "出生:", "出生："));
-        if (birthLabel != null) {
-            String birth = findBirthdayAroundLabel(birthLabel, remainingItems, itemByBox);
-            if (birth != null) {
-                info.setBirthday(birth);
-                final String bFinal = birth;
-                remainingItems.removeIf(it -> {
-                    String t = it.getText();
-                    if (t == null) {
-                        return false;
-                    }
-                    String clean = t.replace(" ", "");
-                    if (clean.contains("出生")) {
-                        return true;
-                    }
-                    String ex = extractBirthday(t);
-                    return bFinal.equals(ex);
-                });
-                itemByBox = buildBoxItemMap(remainingItems);
-            }
-        }
-
-        // 如果生日没识别到，则尝试从身份证号码中推断
-        if (info.getBirthday() == null && info.getIdNumber() != null && info.getIdNumber().length() >= 14) {
-            String id = info.getIdNumber();
-            String yyyyMMdd = id.substring(6, 14);
-            info.setBirthday(formatBirthdayFromId(yyyyMMdd));
-        }
-
-        // 6. 住址（从“住址”右侧开始，向下若干行拼接）
-        OcrItem addressLabel = findAddressLabel(remainingItems);
-        if (addressLabel != null) {
-            String addr = collectAddress(addressLabel, remainingItems, itemByBox);
-            info.setAddress(addr);
-        }
-
-        return info;
-    }
-
-    // ----------------- 文本与 Box 相关的工具方法 -----------------
-
-    private String normalizeText(String text) {
-        if (text == null) {
-            return "";
-        }
-        return text.replace(" ", "")
-                .replaceAll("[^0-9Xx\\u4e00-\\u9fa5]", "");
-    }
-
-    /**
-     * 从包含"姓名"的文本中提取姓名值（提取"姓名"后面的所有中文字符）
-     */
-    private String extractNameAfterLabel(String text) {
-        if (text == null || text.isEmpty()) {
-            return null;
-        }
-        int nameIndex = text.indexOf("姓名");
-        if (nameIndex >= 0) {
-            String name = text.substring(nameIndex + 2);
-            return name.isEmpty() ? null : name;
-        }
-        int surnameIndex = text.indexOf("姓");
-        if (surnameIndex >= 0 && surnameIndex < text.length() - 1) {
-            String name = text.substring(surnameIndex + 1);
-            return name.isEmpty() ? null : name;
-        }
-        int givenNameIndex = text.indexOf("名");
-        if (givenNameIndex >= 0 && givenNameIndex < text.length() - 1) {
-            String name = text.substring(givenNameIndex + 1);
-            return name.isEmpty() ? null : name;
-        }
-        // 如果都没找到，返回原文本（可能已经是纯姓名）
-        return text;
-    }
-
-    private String cleanGender(String text) {
-        if (text == null) {
-            return null;
-        }
-        String clean = text.replace(" ", "");
-        if (clean.contains("男")) {
-            return "男";
-        }
-        if (clean.contains("女")) {
-            return "女";
-        }
-        return null;
-    }
-
-    /**
-     * 按“右 → 上 → 下 → 全局”顺序查找性别信息
-     */
-    private String findGenderAroundLabel(OcrItem genderLabel, List<OcrItem> allItems, Map<OcrBox, OcrItem> itemByBox) {
-        if (genderLabel == null || genderLabel.getOcrBox() == null || allItems == null || allItems.isEmpty()) {
-            return null;
-        }
-        // 情况一：标签所在框本身包含“性别+男/女”
-        String selfGender = cleanGender(genderLabel.getText());
-        if (selfGender != null) {
-            return selfGender;
-        }
-
-        List<OcrBox> boxList = allItems.stream()
-                .map(OcrItem::getOcrBox)
-                .filter(Objects::nonNull)
-                .collect(Collectors.toList());
-
-        OcrBox anchorBox = genderLabel.getOcrBox();
-
-        String gender = findGenderByDirection(anchorBox, itemByBox, boxList, BoxUtils.Direction.RIGHT, 3);
-        if (gender != null) {
-            return gender;
-        }
-        gender = findGenderByDirection(anchorBox, itemByBox, boxList, BoxUtils.Direction.UP, 3);
-        if (gender != null) {
-            return gender;
-        }
-        gender = findGenderByDirection(anchorBox, itemByBox, boxList, BoxUtils.Direction.DOWN, 3);
-        if (gender != null) {
-            return gender;
-        }
-
-        return findNearbyFallback(anchorBox, allItems, this::cleanGender);
-    }
-
-    /**
-     * 按指定方向在若干个最近框中查找性别
-     */
-    private String findGenderByDirection(OcrBox anchorBox,
-                                         Map<OcrBox, OcrItem> itemByBox,
-                                         List<OcrBox> boxList,
-                                         BoxUtils.Direction direction,
-                                         int limit) {
-        List<OcrBox> neighbors = BoxUtils.findNearestBoxes(anchorBox, boxList, direction, limit);
-        if (neighbors == null || neighbors.isEmpty()) {
-            return null;
-        }
-        for (OcrBox neighbor : neighbors) {
-            OcrItem item = itemByBox.get(neighbor);
-            if (item == null) {
-                continue;
-            }
-            String g = cleanGender(item.getText());
-            if (g != null) {
-                return g;
-            }
-        }
-        return null;
-    }
-
-    /**
-     * 按“右 → 上 → 下 → 全局”顺序查找民族信息
-     */
-    private String findEthnicityAroundLabel(OcrItem ethnicLabel, List<OcrItem> allItems, Map<OcrBox, OcrItem> itemByBox) {
-        if (ethnicLabel == null || ethnicLabel.getOcrBox() == null || allItems == null || allItems.isEmpty()) {
-            return null;
-        }
-
-        String selfEthnic = extractEthnicity(ethnicLabel.getText());
-        if (selfEthnic != null) {
-            return selfEthnic;
-        }
-
-        List<OcrBox> boxList = allItems.stream()
-                .map(OcrItem::getOcrBox)
-                .filter(Objects::nonNull)
-                .collect(Collectors.toList());
-
-        OcrBox anchorBox = ethnicLabel.getOcrBox();
-
-        String ethnicity = findEthnicityByDirection(anchorBox, itemByBox, boxList, BoxUtils.Direction.RIGHT, 3);
-        if (ethnicity != null) {
-            return ethnicity;
-        }
-        ethnicity = findEthnicityByDirection(anchorBox, itemByBox, boxList, BoxUtils.Direction.UP, 3);
-        if (ethnicity != null) {
-            return ethnicity;
-        }
-        ethnicity = findEthnicityByDirection(anchorBox, itemByBox, boxList, BoxUtils.Direction.DOWN, 3);
-        if (ethnicity != null) {
-            return ethnicity;
-        }
-
-        return findNearbyFallback(anchorBox, allItems, this::extractEthnicity);
-    }
-
-    /**
-     * 按指定方向在若干个最近框中查找民族
-     */
-    private String findEthnicityByDirection(OcrBox anchorBox,
-                                            Map<OcrBox, OcrItem> itemByBox,
-                                            List<OcrBox> boxList,
-                                            BoxUtils.Direction direction,
-                                            int limit) {
-        List<OcrBox> neighbors = BoxUtils.findNearestBoxes(anchorBox, boxList, direction, limit);
-        if (neighbors == null || neighbors.isEmpty()) {
-            return null;
-        }
-        for (OcrBox neighbor : neighbors) {
-            OcrItem item = itemByBox.get(neighbor);
-            if (item == null) {
-                continue;
-            }
-            String e = extractEthnicity(item.getText());
-            if (e != null) {
-                return e;
-            }
-        }
-        return null;
-    }
-
-    /**
-     * 从文本中提取并纠正民族信息
-     */
-    private String extractEthnicity(String ocrText) {
-        if (ocrText == null || ocrText.isEmpty()) {
-            return null;
-        }
-        String cleanText = ocrText.replaceAll("[^\\u4e00-\\u9fa5]", "");
-        cleanText = cleanText.replaceAll("^民族", "");
-        for (String ethnic : IdCardOcrUtils.ETHNIC_SET) {
-            if (cleanText.contains(ethnic)) {
-                return ethnic;
-            }
-        }
-        return null;
-    }
-
-    /**
-     * 兜底时仅在标签附近寻找合法候选，避免被远处噪声或少数民族文字误带偏。
-     */
-    private String findNearbyFallback(OcrBox anchorBox,
-                                      List<OcrItem> allItems,
-                                      Function<String, String> extractor) {
-        if (anchorBox == null || allItems == null || allItems.isEmpty() || extractor == null) {
-            return null;
-        }
-
-        double anchorWidth = boxWidth(anchorBox);
-        double anchorHeight = boxHeight(anchorBox);
-        Point anchorCenter = boxCenter(anchorBox);
-
-        OcrItem bestItem = null;
-        double bestScore = Double.MAX_VALUE;
-
-        for (OcrItem item : allItems) {
-            if (item == null || item.getOcrBox() == null) {
-                continue;
-            }
-            String extracted = extractor.apply(item.getText());
-            if (extracted == null) {
-                continue;
-            }
-
-            Point candidateCenter = boxCenter(item.getOcrBox());
-            double dx = Math.abs(candidateCenter.getX() - anchorCenter.getX());
-            double dy = Math.abs(candidateCenter.getY() - anchorCenter.getY());
-
-            // 性别/民族通常紧邻标签，限制在局部窗口内做兜底。
-            if (dx > anchorWidth * 5.0 || dy > anchorHeight * 2.5) {
-                continue;
-            }
-
-            double score = dy * 2.0 + dx;
-            if (score < bestScore) {
-                bestScore = score;
-                bestItem = item;
-            }
-        }
-
-        return bestItem == null ? null : extractor.apply(bestItem.getText());
-    }
-
-    /**
-     * 按“自身 → 右 → 下 → 上 → 全局”顺序查找出生日期
-     */
-
-    private String findBirthdayAroundLabel(OcrItem birthLabel, List<OcrItem> allItems, Map<OcrBox, OcrItem> itemByBox) {
-        if (birthLabel == null || birthLabel.getOcrBox() == null || allItems == null || allItems.isEmpty()) {
-            return null;
-        }
-
-        String fromSelf = extractBirthday(birthLabel.getText());
-        if (fromSelf != null) {
-            return fromSelf;
-        }
-
-        List<OcrBox> boxList = allItems.stream()
-                .map(OcrItem::getOcrBox)
-                .filter(Objects::nonNull)
-                .collect(Collectors.toList());
-
-        OcrBox anchorBox = birthLabel.getOcrBox();
-
-        String fromRight = findBirthdayByDirection(anchorBox, itemByBox, boxList, BoxUtils.Direction.RIGHT, 4);
-        if (fromRight != null) {
-            return fromRight;
-        }
-
-        String fromDown = findBirthdayByDirection(anchorBox, itemByBox, boxList, BoxUtils.Direction.DOWN, 4);
-        if (fromDown != null) {
-            return fromDown;
-        }
-
-        String fromUp = findBirthdayByDirection(anchorBox, itemByBox, boxList, BoxUtils.Direction.UP, 3);
-        if (fromUp != null) {
-            return fromUp;
-        }
-
-        for (OcrItem item : allItems) {
-            String b = extractBirthday(item.getText());
-            if (b != null) {
-                return b;
-            }
-        }
-        return null;
-    }
-
-    /**
-     * 按指定方向在若干最近框中尝试组合/单独解析出生日期
-     */
-    private String findBirthdayByDirection(OcrBox anchorBox,
-                                           Map<OcrBox, OcrItem> itemByBox,
-                                           List<OcrBox> boxList,
-                                           BoxUtils.Direction direction,
-                                           int limit) {
-        List<OcrBox> neighbors = BoxUtils.findNearestBoxes(anchorBox, boxList, direction, limit);
-        if (neighbors == null || neighbors.isEmpty()) {
-            return null;
-        }
-
-        List<OcrItem> neighborItems = neighbors.stream()
-                .map(itemByBox::get)
-                .filter(Objects::nonNull)
-                .collect(Collectors.toList());
-
-        if (neighborItems.isEmpty()) {
-            return null;
-        }
-
-        for (OcrItem item : neighborItems) {
-            String b = extractBirthday(item.getText());
-            if (b != null) {
-                return b;
-            }
-        }
-
-        StringBuilder sb = new StringBuilder();
-        for (OcrItem item : neighborItems) {
-            if (item.getText() != null) {
-                sb.append(item.getText().replace(" ", ""));
-            }
-        }
-        String merged = sb.toString();
-        if (!merged.isEmpty()) {
-            String b = extractBirthday(merged);
-            if (b != null) {
-                return b;
-            }
-        }
-        return null;
-    }
-
-    /**
-     * 从文本中提取身份证号（优先 18 位二代证）
-     */
-    private String extractIdNumber(String text) {
-        if (text == null) {
-            return null;
-        }
-        String candidate = text.toUpperCase();
-        Matcher m = Pattern.compile("[0-9X]{15,18}").matcher(candidate);
-        while (m.find()) {
-            String id = m.group();
-            if (id.length() == 18) {
-                return id;
-            }
-        }
-        return null;
-    }
-
-    /**
-     * 从文本中提取生日（标准化为 yyyy-MM-dd）
-     */
-    private String extractBirthday(String text) {
-        if (text == null) {
-            return null;
-        }
-        String clean = text.replace(" ", "");
-
-        Matcher m1 = BIRTHDAY_WITH_SEPARATORS.matcher(clean);
-        if (m1.find()) {
-            String birthday = normalizeBirthday(m1.group(1), m1.group(2), m1.group(3));
-            if (birthday != null) {
-                return birthday;
-            }
-        }
-
-        Matcher m2 = BIRTHDAY_COMPACT.matcher(clean);
-        if (m2.find()) {
-            String birthday = normalizeBirthday(m2.group(1), m2.group(2), m2.group(3));
-            if (birthday != null) {
-                return birthday;
-            }
-        }
-        return null;
-    }
-
-    private String normalizeBirthday(String raw) {
-        String clean = raw.replace("年", "-")
-                .replace("月", "-")
-                .replace("日", "")
-                .replace("/", "-")
-                .replace(".", "-");
-        String[] parts = clean.split("-");
-        if (parts.length != 3) {
-            return null;
-        }
-        String y = parts[0];
-        String m = parts[1].length() == 1 ? "0" + parts[1] : parts[1];
-        String d = parts[2].length() == 1 ? "0" + parts[2] : parts[2];
-        return y + "-" + m + "-" + d;
-    }
-
-    private String normalizeBirthday(String year, String month, String day) {
-        if (year == null || month == null || day == null) {
-            return null;
-        }
-        String normalizedMonth = month.length() == 1 ? "0" + month : month;
-        String normalizedDay = day.length() == 1 ? "0" + day : day;
-        try {
-            LocalDate date = LocalDate.of(
-                    Integer.parseInt(year),
-                    Integer.parseInt(normalizedMonth),
-                    Integer.parseInt(normalizedDay)
-            );
-            return date.toString();
-        } catch (DateTimeException | NumberFormatException e) {
-            return null;
-        }
-    }
-
-    private String formatBirthdayFromId(String yyyymmdd) {
-        if (yyyymmdd == null || yyyymmdd.length() != 8) {
-            return null;
-        }
-        String y = yyyymmdd.substring(0, 4);
-        String m = yyyymmdd.substring(4, 6);
-        String d = yyyymmdd.substring(6, 8);
-        return y + "-" + m + "-" + d;
-    }
-
-    private Point boxCenter(OcrBox box) {
-        float[] pts = box.toFloatArray();
-        float cx = (pts[0] + pts[2] + pts[4] + pts[6]) / 4;
-        float cy = (pts[1] + pts[3] + pts[5] + pts[7]) / 4;
-        return new Point(cx, cy);
-    }
-
-    private double boxWidth(OcrBox box) {
-        float[] pts = box.toFloatArray();
-        float minX = Math.min(Math.min(pts[0], pts[2]), Math.min(pts[4], pts[6]));
-        float maxX = Math.max(Math.max(pts[0], pts[2]), Math.max(pts[4], pts[6]));
-        return Math.max(1.0, maxX - minX);
-    }
-
-    private double boxHeight(OcrBox box) {
-        float[] pts = box.toFloatArray();
-        float minY = Math.min(Math.min(pts[1], pts[3]), Math.min(pts[5], pts[7]));
-        float maxY = Math.max(Math.max(pts[1], pts[3]), Math.max(pts[5], pts[7]));
-        return Math.max(1.0, maxY - minY);
-    }
-
-    /**
-     * 过滤掉面积明显小于整体的检测框，粗略去噪
-     */
-    private List<OcrItem> filterSmallBoxes(List<OcrItem> items) {
-        List<OcrItem> result = IdCardOcrUtils.filterSmallBoxes(items, 0.15);
-        log.debug("身份证正面解析：过滤小框完成，原始数量={}，过滤后数量={}", items == null ? 0 : items.size(), result == null ? 0 : result.size());
-        return result;
-    }
-
-    /**
-     * 基于 BoxUtils，从锚点右侧找到最近的一个文本框
-     */
-    private OcrItem findNearestRightItem(OcrItem anchor, List<OcrItem> allItems, Map<OcrBox, OcrItem> itemByBox) {
-        if (anchor == null || anchor.getOcrBox() == null) {
-            return null;
-        }
-        List<OcrBox> boxList = allItems.stream()
-                .map(OcrItem::getOcrBox)
-                .filter(Objects::nonNull)
-                .collect(Collectors.toList());
-
-        OcrBox nearest = BoxUtils.findNearestBox(anchor.getOcrBox(), boxList, BoxUtils.Direction.RIGHT);
-        if (nearest == null) {
-            return null;
-        }
-        return itemByBox.get(nearest);
-    }
-
-    /**
-     * 查找包含标签关键字的 item
-     */
-    private OcrItem findLabel(List<OcrItem> allItems, List<String> keywords) {
-        for (OcrItem item : allItems) {
-            String text = item.getText();
-            if (text == null) {
-                continue;
-            }
-            String clean = text.replace(" ", "");
-            for (String kw : keywords) {
-                if (clean.contains(kw)) {
-                    return item;
-                }
-            }
-        }
-        return null;
-    }
-
-    private OcrItem findAddressLabel(List<OcrItem> allItems) {
-        OcrItem direct = findLabel(allItems, Arrays.asList("住址", "地址", "住址:", "住址："));
-        if (direct != null) {
-            return direct;
-        }
-        if (allItems == null) {
-            return null;
-        }
-        for (OcrItem item : allItems) {
-            String text = item.getText();
-            if (text == null) {
-                continue;
-            }
-            String clean = text.replace(" ", "");
-            if (looksLikeAddressLabel(clean)) {
-                return item;
-            }
-        }
-        return null;
-    }
-
-    private boolean looksLikeAddressLabel(String text) {
-        if (text == null || text.length() < 2) {
-            return false;
-        }
-        if (text.contains("址") && (text.startsWith("住") || text.startsWith("佳") || text.startsWith("往"))) {
-            return true;
-        }
-        return false;
-    }
-
-    /**
-     * 拼接地址：
-     * 1. 从“住址”右侧开始，整行向右拼接多个检测框
-     * 2. 继续向下拼接若干行同一列附近的文本
-     * 3. 若某一行疑似“公民身份号码”行，则终止
-     */
-    private String collectAddress(OcrItem addressLabel, List<OcrItem> allItems, Map<OcrBox, OcrItem> itemByBox) {
-        StringBuilder sb = new StringBuilder();
-        if (addressLabel == null || addressLabel.getOcrBox() == null || allItems == null || allItems.isEmpty()) {
-            return "";
-        }
-
-        List<OcrBox> boxList = allItems.stream()
-                .map(OcrItem::getOcrBox)
-                .filter(Objects::nonNull)
-                .collect(Collectors.toList());
-
-        Set<OcrBox> usedBoxes = new HashSet<>();
-
-        String labelAddressText = extractAddressAfterLabel(addressLabel.getText());
-        if (!labelAddressText.isEmpty()) {
-            appendMergedText(sb, labelAddressText);
-            usedBoxes.add(addressLabel.getOcrBox());
-        }
-
-        OcrItem firstLineItem = findNearestRightItem(addressLabel, allItems, itemByBox);
-        if (firstLineItem != null && firstLineItem.getOcrBox() != null) {
-            String firstLineText = buildAddressLine(firstLineItem.getOcrBox(), itemByBox, boxList, usedBoxes);
-            if (!firstLineText.isEmpty() && !isIdNumberLine(firstLineText)) {
-                appendMergedText(sb, firstLineText);
-            } else if (isIdNumberLine(firstLineText)) {
-                return sb.toString();
-            }
-        }
-
-        OcrBox current = firstLineItem != null && firstLineItem.getOcrBox() != null
-                ? firstLineItem.getOcrBox()
-                : addressLabel.getOcrBox();
-
-        for (int i = 0; i < 3; i++) {
-            List<OcrBox> downs = BoxUtils.findNearestBoxes(
-                    current,
-                    boxList,
-                    BoxUtils.Direction.DOWN,
-                    1
-            );
-            if (downs == null || downs.isEmpty()) {
-                break;
-            }
-            OcrBox down = downs.get(0);
-            if (!isLikelyAddressContinuation(current, down, itemByBox)) {
-                break;
-            }
-            String lineText = buildAddressLine(down, itemByBox, boxList, usedBoxes);
-            if (lineText.isEmpty()) {
-                break;
-            }
-            if (isIdNumberLine(lineText)) {
-                break;
-            }
-            appendMergedText(sb, lineText);
-            current = down;
-        }
-
-        return sb.toString();
-    }
-
-    private String extractAddressAfterLabel(String text) {
-        if (text == null || text.isEmpty()) {
-            return "";
-        }
-        String clean = text.replace(" ", "");
-        int idx = clean.indexOf("住址");
-        if (idx >= 0) {
-            return clean.substring(idx + 2);
-        }
-        idx = clean.indexOf("佳址");
-        if (idx >= 0) {
-            return clean.substring(idx + 2);
-        }
-        idx = clean.indexOf("往址");
-        if (idx >= 0) {
-            return clean.substring(idx + 2);
-        }
-        idx = clean.indexOf("地址");
-        if (idx >= 0) {
-            return clean.substring(idx + 2);
-        }
-        return "";
-    }
-
-    private void appendMergedText(StringBuilder sb, String nextText) {
-        if (nextText == null || nextText.isEmpty()) {
-            return;
-        }
-        if (sb.length() == 0) {
-            sb.append(nextText);
-            return;
-        }
-        String existing = sb.toString();
-        int maxOverlap = Math.min(existing.length(), nextText.length());
-        for (int overlap = maxOverlap; overlap > 0; overlap--) {
-            if (existing.regionMatches(existing.length() - overlap, nextText, 0, overlap)) {
-                sb.append(nextText.substring(overlap));
-                return;
-            }
-        }
-        sb.append(nextText);
-    }
-
-    private boolean isLikelyAddressContinuation(OcrBox current, OcrBox candidate, Map<OcrBox, OcrItem> itemByBox) {
-        if (current == null || candidate == null) {
-            return false;
-        }
-        OcrItem candidateItem = itemByBox.get(candidate);
-        if (candidateItem == null) {
-            return false;
-        }
-        String text = candidateItem.getText();
-        if (text == null || text.trim().isEmpty()) {
-            return false;
-        }
-        String compact = text.replace(" ", "");
-        if (!compact.matches(".*[\\u4e00-\\u9fa5].*")) {
-            return false;
-        }
-        if (compact.matches("[A-Za-z]+")) {
-            return false;
-        }
-
-        double currentBottom = Math.max(Math.max(current.getBottomLeft().getY(), current.getBottomRight().getY()),
-                Math.max(current.getTopLeft().getY(), current.getTopRight().getY()));
-        double candidateTop = Math.min(Math.min(candidate.getTopLeft().getY(), candidate.getTopRight().getY()),
-                Math.min(candidate.getBottomLeft().getY(), candidate.getBottomRight().getY()));
-        double verticalGap = candidateTop - currentBottom;
-        double allowedGap = Math.max(boxHeight(current), boxHeight(candidate)) * 1.2;
-        if (verticalGap > allowedGap) {
-            return false;
-        }
-
-        double currentCenterX = boxCenter(current).getX();
-        double candidateCenterX = boxCenter(candidate).getX();
-        double centerDeltaX = Math.abs(candidateCenterX - currentCenterX);
-        double allowedDeltaX = Math.max(boxWidth(current), boxWidth(candidate)) * 0.9;
-        return centerDeltaX <= allowedDeltaX;
-    }
-
-    /**
-     * 根据某一行的起始框，向右拼接同一行上的多个检测框文本
-     */
-    private String buildAddressLine(OcrBox rowAnchor,
-                                    Map<OcrBox, OcrItem> itemByBox,
-                                    List<OcrBox> boxList,
-                                    Set<OcrBox> usedBoxes) {
-        if (rowAnchor == null) {
-            return "";
-        }
-        StringBuilder sb = new StringBuilder();
-
-        OcrItem anchorItem = itemByBox.get(rowAnchor);
-        if (anchorItem != null && anchorItem.getText() != null && !usedBoxes.contains(rowAnchor)) {
-            sb.append(anchorItem.getText().replace(" ", ""));
-            usedBoxes.add(rowAnchor);
-        }
-
-        List<OcrBox> rightBoxes = BoxUtils.findNearestBoxes(rowAnchor, boxList, BoxUtils.Direction.RIGHT, 8);
-        if (rightBoxes != null && !rightBoxes.isEmpty()) {
-            for (OcrBox rb : rightBoxes) {
-                if (usedBoxes.contains(rb)) {
-                    continue;
-                }
-                OcrItem item = itemByBox.get(rb);
-                if (item != null && item.getText() != null) {
-                    sb.append(item.getText().replace(" ", ""));
-                    usedBoxes.add(rb);
-                }
-            }
-        }
-
-        return sb.toString();
-    }
-
-    /**
-     * 判断一行文本是否疑似“公民身份号码”行
-     */
-    private boolean isIdNumberLine(String text) {
-        if (text == null || text.isEmpty()) {
-            return false;
-        }
-        String norm = normalizeText(text);
-        if (norm.contains("公民身份号码") || norm.contains("公民身份號碼") || norm.toUpperCase().contains("ID")) {
-            return true;
-        }
-        return extractIdNumber(norm) != null;
-    }
-
-    private Map<OcrBox, OcrItem> buildBoxItemMap(List<OcrItem> items) {
-        Map<OcrBox, OcrItem> itemByBox = new IdentityHashMap<>();
-        if (items == null) {
-            return itemByBox;
-        }
-        for (OcrItem item : items) {
-            if (item != null && item.getOcrBox() != null) {
-                itemByBox.put(item.getOcrBox(), item);
-            }
-        }
-        return itemByBox;
-    }
-}

ocr/src/main/java/cn/smartjavaai/ocr/idcard/IdCardOcrUtils.java

@@ -1,68 +0,0 @@
-package cn.smartjavaai.ocr.idcard;
-
-import cn.smartjavaai.ocr.entity.OcrBox;
-import cn.smartjavaai.ocr.entity.OcrItem;
-
-import java.util.Arrays;
-import java.util.Collections;
-import java.util.HashSet;
-import java.util.List;
-import java.util.Objects;
-import java.util.Set;
-import java.util.stream.Collectors;
-
-/**
- * 身份证 OCR 解析相关的通用工具。
- */
-final class IdCardOcrUtils {
-
-    static final Set<String> ETHNIC_SET = Collections.unmodifiableSet(new HashSet<>(Arrays.asList(
-            "汉", "蒙古", "回", "藏", "维吾尔", "苗", "彝", "壮", "布依", "朝鲜", "满", "侗",
-            "瑶", "白", "土家", "哈尼", "哈萨克", "傣", "黎", "傈僳", "佤", "畲", "高山",
-            "拉祜", "水", "东乡", "纳西", "景颇", "柯尔克孜", "土", "达斡尔", "仫佬",
-            "羌", "布朗", "撒拉", "毛南", "仡佬", "锡伯", "阿昌", "普米", "塔吉克", "怒",
-            "乌孜别克", "俄罗斯", "鄂温克", "德昂", "保安", "裕固", "京", "塔塔尔", "独龙",
-            "鄂伦春", "赫哲", "门巴", "珞巴", "基诺"
-    )));
-
-    private IdCardOcrUtils() {
-    }
-
-    static List<OcrItem> filterSmallBoxes(List<OcrItem> items, double factor) {
-        if (items == null || items.isEmpty()) {
-            return items;
-        }
-
-        List<Double> areas = items.stream()
-                .map(OcrItem::getOcrBox)
-                .filter(Objects::nonNull)
-                .map(IdCardOcrUtils::estimateBoxArea)
-                .sorted()
-                .collect(Collectors.toList());
-
-        if (areas.size() < 5) {
-            return items;
-        }
-
-        double median = areas.get(areas.size() / 2);
-        double threshold = median * factor;
-        List<OcrItem> result = items.stream()
-                .filter(it -> it.getOcrBox() == null || estimateBoxArea(it.getOcrBox()) >= threshold)
-                .collect(Collectors.toList());
-        return result.isEmpty() ? items : result;
-    }
-
-    static double estimateBoxArea(OcrBox box) {
-        if (box == null) {
-            return 0.0;
-        }
-        float[] pts = box.toFloatArray();
-        float minX = Math.min(Math.min(pts[0], pts[2]), Math.min(pts[4], pts[6]));
-        float minY = Math.min(Math.min(pts[1], pts[3]), Math.min(pts[5], pts[7]));
-        float maxX = Math.max(Math.max(pts[0], pts[2]), Math.max(pts[4], pts[6]));
-        float maxY = Math.max(Math.max(pts[1], pts[3]), Math.max(pts[5], pts[7]));
-        double width = Math.max(1.0, maxX - minX);
-        double height = Math.max(1.0, maxY - minY);
-        return width * height;
-    }
-}

ocr/src/main/java/cn/smartjavaai/ocr/idcard/IdCardParser.java

@@ -1,15 +0,0 @@
-package cn.smartjavaai.ocr.idcard;
-
-import cn.smartjavaai.ocr.entity.OcrInfo;
-
-/**
- * 身份证解析接口，用于从 OCR 结果中解析结构化字段。
- */
-public interface IdCardParser<T> {
-
-    /**
-     * 从 OCR 结果中解析出指定类型的身份证信息
-     */
-    T parse(OcrInfo ocrInfo);
-}
-

ocr/src/main/java/cn/smartjavaai/ocr/idcard/IdCardPreprocessListener.java

@@ -1,29 +0,0 @@
-package cn.smartjavaai.ocr.idcard;
-
-import ai.djl.modality.cv.Image;
-import cn.smartjavaai.ocr.entity.OcrInfo;
-
-/**
- * 身份证预处理调试监听器。
- *
- * 在预处理或识别流程中，只有当某个阶段真的产出了中间结果时，才会回调当前图像，
- * 便于测试环境中将中间结果保存到磁盘进行对比和调试。
- *
- * 生产环境可不设置监听器，完全不影响正常流程。
- */
-public interface IdCardPreprocessListener {
-
-    /**
-     * 方向矫正完成后回调。
-     * 如果方向检测结果为 0 且未发生旋转，则不会调用。
-     */
-    default void onAfterDirection(Image image) {}
-
-    /**
-     * OCR 识别完成后回调。
-     *
-     * 传入的 image 为预处理后的图片副本，已绘制 OCR 检测框，便于直接保存调试结果。
-     * 如果识别流程失败或未进入识别阶段，则不会调用。
-     */
-    default void onAfterRecognize(Image image, OcrInfo ocrInfo) {}
-}

ocr/src/main/java/cn/smartjavaai/ocr/idcard/IdCardPreprocessResult.java

@@ -1,26 +0,0 @@
-package cn.smartjavaai.ocr.idcard;
-
-import ai.djl.modality.cv.Image;
-import cn.smartjavaai.ocr.entity.OcrBox;
-import lombok.Data;
-import lombok.experimental.Accessors;
-
-import java.util.List;
-
-/**
- * 身份证预处理结果。
- *
- * - processedImage: 预处理后的图像；若无需旋转，则通常与原图一致
- * - reusableBoxes: 可复用的文本检测框；仅在无需旋转时可直接复用
- * - rotated: 是否进行了整图旋转
- */
-@Data
-@Accessors(chain = true)
-public class IdCardPreprocessResult {
-
-    private Image processedImage;
-
-    private List<OcrBox> reusableBoxes;
-
-    private boolean rotated;
-}

ocr/src/main/java/cn/smartjavaai/ocr/idcard/IdCardPreprocessor.java

@@ -1,227 +0,0 @@
-package cn.smartjavaai.ocr.idcard;
-
-import ai.djl.modality.cv.Image;
-import cn.smartjavaai.ocr.entity.OcrBox;
-import cn.smartjavaai.ocr.entity.OcrItem;
-import cn.smartjavaai.ocr.enums.AngleEnum;
-import cn.smartjavaai.ocr.model.common.detect.OcrCommonDetModel;
-import cn.smartjavaai.ocr.model.common.direction.OcrDirectionModel;
-import cn.smartjavaai.ocr.utils.OcrUtils;
-import lombok.extern.slf4j.Slf4j;
-import org.opencv.core.Mat;
-
-import java.util.ArrayList;
-import java.util.List;
-
-/**
- * 身份证图像预处理：方向矫正（0/90/180/270°）。
- *
- * - 方向矫正：使用 OcrDirectionModel 检测整图方向并旋转至正向；
- */
-@Slf4j
-public class IdCardPreprocessor {
-
-    /**
-     * 预处理：
-     * 1. 方向矫正（若提供 directionModel）：纠正 0/90/180/270° 整体方向；
-     * 2. 若提供 listener，则在阶段结束后回调当前图像，便于测试保存中间结果。
-     */
-    public Image preprocess(Image src,
-                            OcrDirectionModel directionModel,
-                            IdCardPreprocessListener listener) {
-        if (src == null) {
-            return src;
-        }
-        if (directionModel != null) {
-            return applyDirectionCorrection(src, directionModel, listener);
-        }
-        return src;
-    }
-
-    /**
-     * 预处理（不关心调试监听器的常规调用入口）。
-     */
-    public Image preprocess(Image src,
-                            OcrDirectionModel directionModel) {
-        return preprocess(src, directionModel, null);
-    }
-
-    /**
-     * 预处理增强版：
-     * 1. 先做文本检测
-     * 2. 过滤明显过小的文本框，降低方向判断噪声
-     * 3. 使用已有文本框做方向检测
-     * 4. 若无需旋转，则直接返回可复用的检测框
-     * 5. 若需要旋转，则返回旋转后的图像，检测框不复用
-     */
-    public IdCardPreprocessResult preprocess(Image src,
-                                             OcrCommonDetModel textDetModel,
-                                             OcrDirectionModel directionModel,
-                                             IdCardPreprocessListener listener) {
-        IdCardPreprocessResult result = new IdCardPreprocessResult()
-                .setProcessedImage(src)
-                .setRotated(false);
-        if (src == null) {
-            return result;
-        }
-        if (textDetModel == null || directionModel == null) {
-            if (directionModel != null) {
-                result.setProcessedImage(preprocess(src, directionModel, listener));
-                result.setRotated(result.getProcessedImage() != src);
-            }
-            return result;
-        }
-        Mat srcMat = null;
-        try {
-            srcMat = cn.smartjavaai.common.utils.ImageUtils.toMat(src).clone();
-            List<OcrBox> detectedBoxes = textDetModel.detect(src);
-            List<OcrBox> filteredBoxes = filterSmallBoxes(detectedBoxes);
-            if (filteredBoxes.isEmpty()) {
-                result.setReusableBoxes(detectedBoxes);
-                return result;
-            }
-            List<OcrItem> directionItems = directionModel.detect(filteredBoxes, srcMat);
-            AngleEnum dominant = dominantAngle(directionItems);
-            if (dominant != null && dominant != AngleEnum.ANGLE_0) {
-                log.debug("方向检测结果为 {}，执行整图旋转矫正。", dominant);
-                Image rotated = OcrUtils.rotateImg(src, dominant);
-                if (listener != null) {
-                    listener.onAfterDirection(rotated);
-                }
-                return result.setProcessedImage(rotated)
-                        .setReusableBoxes(null)
-                        .setRotated(true);
-            }
-            return result.setReusableBoxes(detectedBoxes);
-        } catch (Exception e) {
-            log.warn("身份证方向矫正失败，使用原图继续。", e);
-            return result;
-        } finally {
-            if (srcMat != null) {
-                srcMat.release();
-            }
-        }
-    }
-
-    /**
-     * 使用方向模型检测整图方向（0/90/180/270°），并将图片旋转至正向。
-     */
-    private Image applyDirectionCorrection(Image src,
-                                           OcrDirectionModel directionModel,
-                                           IdCardPreprocessListener listener) {
-        if (src == null || directionModel == null) {
-            return src;
-        }
-        try {
-            long detectStart = System.nanoTime();
-            List<OcrItem> items = directionModel.detect(src);
-            log.debug("身份证方向模型调用耗时={}ms, 检测框数量={}", elapsedMillis(detectStart), items == null ? 0 : items.size());
-            log.debug("方向检测结果：{}", items);
-            AngleEnum dominant = dominantAngle(items);
-            if (dominant != null && dominant != AngleEnum.ANGLE_0) {
-                log.info("方向检测结果为 {}，执行整图旋转矫正。", dominant);
-                long rotateStart = System.nanoTime();
-                Image rotated = OcrUtils.rotateImg(src, dominant);
-                log.debug("身份证整图旋转耗时={}ms", elapsedMillis(rotateStart));
-                if (listener != null) {
-                    listener.onAfterDirection(rotated);
-                }
-                return rotated;
-            }
-        } catch (Exception e) {
-            log.warn("身份证方向矫正失败，使用原图继续。", e);
-        }
-        return src;
-    }
-
-    /**
-     * 从方向检测结果中取主角度：
-     * 1. 先过滤掉明显的小框，减少碎框噪声；
-     * 2. 再按 面积 * score 做加权投票；
-     * 3. 若过滤后为空，则退化为对全部框做加权投票。
-     */
-    private AngleEnum dominantAngle(List<OcrItem> items) {
-        if (items == null || items.isEmpty()) {
-            return null;
-        }
-        double maxArea = 0.0;
-        for (OcrItem item : items) {
-            if (item == null || item.getAngle() == null || item.getOcrBox() == null) {
-                continue;
-            }
-            maxArea = Math.max(maxArea, estimateBoxArea(item.getOcrBox()));
-        }
-        if (maxArea <= 0.0) {
-            return weightedVote(items, 0.0);
-        }
-
-        double minArea = maxArea * 0.15;
-        AngleEnum dominant = weightedVote(items, minArea);
-        if (dominant != null) {
-            return dominant;
-        }
-        return weightedVote(items, 0.0);
-    }
-
-    private AngleEnum weightedVote(List<OcrItem> items, double minArea) {
-        AngleEnum first = null;
-        double weight0 = 0.0, weight90 = 0.0, weight180 = 0.0, weight270 = 0.0;
-        for (OcrItem item : items) {
-            if (item == null || item.getAngle() == null || item.getOcrBox() == null) {
-                continue;
-            }
-            double area = estimateBoxArea(item.getOcrBox());
-            if (area < minArea) {
-                continue;
-            }
-            double score = item.getScore() > 0 ? item.getScore() : 1.0;
-            double weight = area * score;
-            AngleEnum angle = item.getAngle();
-            if (first == null) {
-                first = angle;
-            }
-            switch (angle) {
-                case ANGLE_0: weight0 += weight; break;
-                case ANGLE_90: weight90 += weight; break;
-                case ANGLE_180: weight180 += weight; break;
-                case ANGLE_270: weight270 += weight; break;
-            }
-        }
-        double maxWeight = Math.max(Math.max(weight0, weight90), Math.max(weight180, weight270));
-        if (maxWeight <= 0.0) {
-            return first;
-        }
-        if (Double.compare(weight90, maxWeight) == 0) return AngleEnum.ANGLE_90;
-        if (Double.compare(weight270, maxWeight) == 0) return AngleEnum.ANGLE_270;
-        if (Double.compare(weight180, maxWeight) == 0) return AngleEnum.ANGLE_180;
-        if (Double.compare(weight0, maxWeight) == 0) return AngleEnum.ANGLE_0;
-        return first;
-    }
-
-    private List<OcrBox> filterSmallBoxes(List<OcrBox> boxes) {
-        if (boxes == null || boxes.isEmpty()) {
-            return boxes;
-        }
-        List<OcrItem> items = new ArrayList<>(boxes.size());
-        for (OcrBox box : boxes) {
-            items.add(new OcrItem(box, AngleEnum.ANGLE_0, 1.0f));
-        }
-        List<OcrItem> filteredItems = IdCardOcrUtils.filterSmallBoxes(items, 0.15);
-        List<OcrBox> filteredBoxes = new ArrayList<>(filteredItems.size());
-        for (OcrItem item : filteredItems) {
-            if (item != null && item.getOcrBox() != null) {
-                filteredBoxes.add(item.getOcrBox());
-            }
-        }
-        return filteredBoxes.isEmpty() ? boxes : filteredBoxes;
-    }
-
-    private double estimateBoxArea(OcrBox box) {
-        return IdCardOcrUtils.estimateBoxArea(box);
-    }
-
-    private long elapsedMillis(long startNanos) {
-        return (System.nanoTime() - startNanos) / 1_000_000;
-    }
-
-}

ocr/src/main/java/cn/smartjavaai/ocr/idcard/IdCardRecognizer.java

@@ -1,44 +0,0 @@
-package cn.smartjavaai.ocr.idcard;
-
-import ai.djl.modality.cv.Image;
-import cn.smartjavaai.ocr.entity.IdCardBackInfo;
-import cn.smartjavaai.ocr.entity.IdCardFrontInfo;
-import cn.smartjavaai.ocr.entity.IdCardInfo;
-import cn.smartjavaai.ocr.entity.OcrInfo;
-
-/**
- * 身份证 OCR 识别服务接口
- *
- * 支持：
- * 1. 直接传入图片，由服务内部完成预处理 + OCR + 解析；
- * 2. 已有 OCR 结果的情况下，仅做单面结构化解析。
- */
-public interface IdCardRecognizer {
-
-    /**
-     * 识别身份证正面（从图片开始）
-     */
-    IdCardFrontInfo recognizeFront(Image image);
-
-    /**
-     * 识别身份证反面（从图片开始）
-     */
-    IdCardBackInfo recognizeBack(Image image);
-
-    /**
-     * 识别身份证正面（仅结构化已有 OCR 结果）
-     */
-    IdCardFrontInfo recognizeFront(OcrInfo ocrInfo);
-
-    /**
-     * 识别身份证反面（仅结构化已有 OCR 结果）
-     */
-    IdCardBackInfo recognizeBack(OcrInfo ocrInfo);
-
-    /**
-     * 同时识别身份证正反面（分别传入两面图片）
-     */
-    IdCardInfo recognizeBoth(Image frontImage, Image backImage);
-
-
-}

ocr/src/main/java/cn/smartjavaai/ocr/idcard/IdCardValidator.java

@@ -1,79 +0,0 @@
-package cn.smartjavaai.ocr.idcard;
-
-import cn.smartjavaai.ocr.entity.IdCardBackInfo;
-import cn.smartjavaai.ocr.entity.IdCardFrontInfo;
-import cn.smartjavaai.ocr.entity.IdCardInfo;
-import lombok.extern.slf4j.Slf4j;
-
-/**
- * 身份证字段校验工具。
- *
- * 提供：
- * - 正面字段完整性与格式校验
- * - 反面字段简单格式校验（预留）
- * - 身份证号码校验位校验
- */
-@Slf4j
-public class IdCardValidator {
-
-    public boolean validateFront(IdCardFrontInfo info) {
-        if (info == null) {
-            return false;
-        }
-        boolean nameOk = info.getName() != null
-                && info.getName().length() >= 2
-                && info.getName().length() <= 4;
-        boolean genderOk = "男".equals(info.getGender()) || "女".equals(info.getGender());
-        boolean ethnicOk = info.getEthnicity() != null && IdCardOcrUtils.ETHNIC_SET.contains(info.getEthnicity());
-        boolean idOk = validateIdNumber(info.getIdNumber());
-
-        log.debug("身份证正面字段校验详情：nameOk={}, genderOk={}, ethnicOk={}, idOk={}",
-                nameOk, genderOk, ethnicOk, idOk);
-        return nameOk && genderOk && ethnicOk && idOk;
-    }
-
-    public boolean validateBack(IdCardBackInfo info) {
-        // 目前只做存在性校验，后续可以根据业务需要增加日期格式、范围等更严格校验
-        if (info == null) {
-            return false;
-        }
-        boolean authorityOk = info.getIssuingAuthority() != null && !info.getIssuingAuthority().isEmpty();
-        boolean validFromOk = info.getValidFrom() != null && !info.getValidFrom().isEmpty();
-        boolean validToOk = info.getValidTo() != null && !info.getValidTo().isEmpty();
-        log.info("身份证反面字段校验详情：authorityOk={}, validFromOk={}, validToOk={}",
-                authorityOk, validFromOk, validToOk);
-        return authorityOk && validFromOk && validToOk;
-    }
-
-    public boolean validate(IdCardInfo info) {
-        if (info == null) {
-            return false;
-        }
-        boolean frontOk = info.getFront() == null || validateFront(info.getFront());
-        boolean backOk = info.getBack() == null || validateBack(info.getBack());
-        return frontOk && backOk;
-    }
-
-    /**
-     * 简单的身份证号码格式 + 校验位校验
-     */
-    public boolean validateIdNumber(String id) {
-        if (id == null) {
-            return false;
-        }
-        String upper = id.toUpperCase();
-        if (!upper.matches("^[1-9]\\d{5}(19|20)\\d{2}(0[1-9]|1[0-2])(0[1-9]|[12]\\d|3[01])\\d{3}[0-9X]$")) {
-            return false;
-        }
-        // 校验位
-        char[] chars = upper.toCharArray();
-        int[] weight = {7, 9, 10, 5, 8, 4, 2, 1, 6, 3, 7, 9, 10, 5, 8, 4, 2};
-        char[] validateCode = {'1', '0', 'X', '9', '8', '7', '6', '5', '4', '3', '2'};
-        int sum = 0;
-        for (int i = 0; i < 17; i++) {
-            sum += (chars[i] - '0') * weight[i];
-        }
-        int mod = sum % 11;
-        return validateCode[mod] == chars[17];
-    }
-}

ocr/src/main/java/cn/smartjavaai/ocr/model/common/detect/OcrCommonDetModelImpl.java

@@ -94,10 +94,8 @@ public class OcrCommonDetModelImpl implements OcrCommonDetModel{
 
     @Override
     public List<OcrBox> detect(Image image){
-        long start = System.nanoTime();
         List<Image> imageList = Collections.singletonList(image);
         List<List<OcrBox>> result = batchDetectDJLImage(imageList);
-        log.debug("文本检测模型单图调用耗时={}ms, 检测框数量={}", elapsedMillis(start), result.isEmpty() ? 0 : result.get(0).size());
         return result.get(0);
     }
 
@@ -194,19 +192,12 @@ public class OcrCommonDetModelImpl implements OcrCommonDetModel{
         if(!ImageUtils.isAllImageSizeEqual(imageList)){
             throw new OcrException("图片尺寸不一致");
         }
-        long totalStart = System.nanoTime();
         Predictor<Image, NDList> predictor = null;
         try (NDManager manager = NDManager.newBaseManager()) {
             predictor = detPredictorPool.borrowObject();
             List<NDList> result = predictor.batchPredict(imageList);
             result.forEach(ndList -> ndList.attach(manager));
-            List<List<OcrBox>> boxes = OcrUtils.convertToOcrBox(result);
+            return OcrUtils.convertToOcrBox(result);
-            int totalBoxes = 0;
-            for (List<OcrBox> boxList : boxes) {
-                totalBoxes += boxList == null ? 0 : boxList.size();
-            }
-            log.debug("文本检测总耗时={}ms, batchSize={}, totalBoxes={}", elapsedMillis(totalStart), imageList.size(), totalBoxes);
-            return boxes;
         } catch (Exception e) {
             throw new OcrException("OCR检测错误", e);
         }finally {
@@ -266,9 +257,5 @@ public class OcrCommonDetModelImpl implements OcrCommonDetModel{
         return fromFactory;
     }
 
-    private long elapsedMillis(long startNanos) {
-        return (System.nanoTime() - startNanos) / 1_000_000;
-    }
-
 
 }

ocr/src/main/java/cn/smartjavaai/ocr/model/common/direction/PPOCRMobileV2ClsModel.java

@@ -108,13 +108,13 @@ public class PPOCRMobileV2ClsModel implements OcrDirectionModel {
         if(Objects.isNull(textDetModel)){
             throw new OcrException("textDetModel is null");
         }
+        //检测文本
         List<OcrBox> boxeList = textDetModel.detect(image);
         if(Objects.isNull(boxeList) || boxeList.isEmpty()){
             throw new OcrException("未检测到文本");
         }
         Mat srcMat = ImageUtils.toMat(image);
-        List<OcrItem> result = detect(boxeList, srcMat);
+        return detect(boxeList, srcMat);
-        return result;
     }
 
 
@@ -274,9 +274,7 @@ public class PPOCRMobileV2ClsModel implements OcrDirectionModel {
         List<Image> imageList = new ArrayList<Image>();
         List<Boolean> isRotatedList = new ArrayList<Boolean>();
         int index = 0;
-        long totalStart = System.nanoTime();
         try (NDManager manager = model.getNDManager().newSubManager()){
-            long prepareStart = System.nanoTime();
             for(int i = 0; i < srcMatList.size(); i++){
                 for (int j = 0; j < boxList.get(i).size(); j++){
                     //透视变换及裁剪
@@ -294,11 +292,8 @@ public class PPOCRMobileV2ClsModel implements OcrDirectionModel {
                     index++;
                 }
             }
-            log.debug("方向模型裁剪预处理耗时={}ms, batchSize={}, textBlocks={}", elapsedMillis(prepareStart), srcMatList.size(), imageList.size());
             List<List<OcrItem>> result = new ArrayList<>();
-            long predictStart = System.nanoTime();
             List<DirectionInfo> directionInfos = batchDetect(imageList);
-            log.debug("方向分类模型调用耗时={}ms, textBlocks={}", elapsedMillis(predictStart), imageList.size());
             //释放
             imageList.forEach(image -> ImageUtils.releaseOpenCVMat(image));
             if(CollectionUtils.isEmpty(directionInfos)){
@@ -332,7 +327,6 @@ public class PPOCRMobileV2ClsModel implements OcrDirectionModel {
                 }
                 result.add(ocrItemList);
             }
-            log.debug("方向模型总耗时={}ms, batchSize={}, textBlocks={}", elapsedMillis(totalStart), srcMatList.size(), index);
             return result;
         }
     }
@@ -341,8 +335,7 @@ public class PPOCRMobileV2ClsModel implements OcrDirectionModel {
         Predictor<Image, DirectionInfo> predictor = null;
         try {
             predictor = predictorPool.borrowObject();
-            List<DirectionInfo> result = predictor.batchPredict(imageList);
+            return predictor.batchPredict(imageList);
-            return result;
         } catch (Exception e) {
             throw new OcrException("OCR检测错误", e);
         }finally {
@@ -406,8 +399,4 @@ public class PPOCRMobileV2ClsModel implements OcrDirectionModel {
     public boolean isFromFactory() {
         return fromFactory;
     }
-
-    private long elapsedMillis(long startNanos) {
-        return (System.nanoTime() - startNanos) / 1_000_000;
-    }
 }

ocr/src/main/java/cn/smartjavaai/ocr/model/common/recognize/OcrCommonRecModel.java

@@ -61,19 +61,6 @@ public interface OcrCommonRecModel extends AutoCloseable{
         throw new UnsupportedOperationException("默认不支持该功能");
     }
 
-    /**
-     * 基于已有文本检测框执行文本识别。
-     * 适合调用方已经完成文本检测，想避免重复检测的场景。
-     *
-     * @param image 原图
-     * @param boxList 已有文本检测框
-     * @param options 识别选项
-     * @return OCR 结果
-     */
-    default OcrInfo recognize(Image image, List<OcrBox> boxList, OcrRecOptions options) {
-        throw new UnsupportedOperationException("默认不支持该功能");
-    }
-
 
     /**
      * 文本检测
@@ -154,18 +141,6 @@ public interface OcrCommonRecModel extends AutoCloseable{
         throw new UnsupportedOperationException("默认不支持该功能");
     }
 
-    /**
-     * 基于已有文本检测框执行批量文本识别。
-     *
-     * @param imageList 原图列表
-     * @param boxList 每张图对应的文本检测框列表
-     * @param options 识别选项
-     * @return OCR 结果
-     */
-    default List<OcrInfo> batchRecognizeDJLImage(List<Image> imageList, List<List<OcrBox>> boxList, OcrRecOptions options) {
-        throw new UnsupportedOperationException("默认不支持该功能");
-    }
-
     default GenericObjectPool<Predictor<Image, String>> getPool() {
         throw new UnsupportedOperationException("默认不支持该功能");
     }

ocr/src/main/java/cn/smartjavaai/ocr/model/common/recognize/OcrCommonRecModelImpl.java

@@ -47,8 +47,6 @@ import java.util.stream.Collectors;
 @Slf4j
 public class OcrCommonRecModelImpl implements OcrCommonRecModel {
 
-    private static final int REC_CHUNK_SIZE = 64;
-
     private GenericObjectPool<Predictor<Image, String>> recPredictorPool;
 
     private OcrRecModelConfig config;
@@ -113,27 +111,10 @@ public class OcrCommonRecModelImpl implements OcrCommonRecModel {
      */
     @Override
     public OcrInfo recognize(Image image, OcrRecOptions options) {
-        long start = System.nanoTime();
         List<OcrInfo> result = batchRecognizeDJLImage(Collections.singletonList(image), options);
         if (CollectionUtils.isEmpty(result)) {
             throw new OcrException("OCR识别结果为空");
         }
-        log.debug("OCR识别单图总耗时={}ms", elapsedMillis(start));
-        return result.get(0);
-    }
-
-    @Override
-    public OcrInfo recognize(Image image, List<OcrBox> boxList, OcrRecOptions options) {
-        long start = System.nanoTime();
-        List<OcrInfo> result = batchRecognizeDJLImage(
-                Collections.singletonList(image),
-                Collections.singletonList(boxList),
-                options
-        );
-        if (CollectionUtils.isEmpty(result)) {
-            throw new OcrException("OCR识别结果为空");
-        }
-        log.debug("OCR识别单图总耗时={}ms", elapsedMillis(start));
         return result.get(0);
     }
 
@@ -231,8 +212,10 @@ public class OcrCommonRecModelImpl implements OcrCommonRecModel {
         //分行判断
         for (int i = 1; i < initList.size(); i++) {
             RotatedBoxCompX tmpBox = new RotatedBoxCompX(initList.get(i).getBox(), initList.get(i).getText());
-            boolean isSameRow = OcrUtils.isSameRow(firstBox.getBox(), tmpBox.getBox());
+            float y1 = firstBox.getBox().toFloatArray()[1];
-            if (isSameRow) {
+            float y2 = tmpBox.getBox().toFloatArray()[1];
+            float dis = Math.abs(y2 - y1);
+            if (dis < 20) { // 认为是同 1 行  - Considered to be in the same line
                 line.add(tmpBox);
             } else { // 换行 - Line break
                 firstBox = tmpBox;
@@ -363,11 +346,6 @@ public class OcrCommonRecModelImpl implements OcrCommonRecModel {
 
     @Override
     public List<OcrInfo> batchRecognizeDJLImage(List<Image> imageList, OcrRecOptions options) {
-        return batchRecognizeDJLImage(imageList, null, options);
-    }
-
-    @Override
-    public List<OcrInfo> batchRecognizeDJLImage(List<Image> imageList, List<List<OcrBox>> boxeList, OcrRecOptions options) {
         if (Objects.isNull(textDetModel)) {
             throw new OcrException("textDetModel is null");
         }
@@ -378,82 +356,64 @@ public class OcrCommonRecModelImpl implements OcrCommonRecModel {
         if (CollectionUtils.isEmpty(imageList)) {
             throw new OcrException("imageList is empty");
         }
-        long totalStart = System.nanoTime();
+        //检测文本
-        List<List<OcrBox>> effectiveBoxList = boxeList;
+        List<List<OcrBox>> boxeList = textDetModel.batchDetectDJLImage(imageList);
-        if (CollectionUtils.isEmpty(effectiveBoxList)) {
+        if (CollectionUtils.isEmpty(boxeList) || boxeList.size() != imageList.size()) {
-            effectiveBoxList = textDetModel.batchDetectDJLImage(imageList);
-        }
-        if (CollectionUtils.isEmpty(effectiveBoxList) || effectiveBoxList.size() != imageList.size()) {
             throw new OcrException("未检测到文本");
         }
         Predictor<Image, String> predictor = null;
         List<OcrInfo> ocrInfoList = new ArrayList<OcrInfo>();
         try (NDManager manager = NDManager.newBaseManager()) {
             predictor = recPredictorPool.borrowObject();
+            List<Image> allImageAlignList = new ArrayList<Image>();
             //检测方向
             if (ocrRecOptions.isEnableDirectionCorrect()) {
                 if (Objects.isNull(directionModel)) {
                     throw new OcrException("请配置方向模型");
                 }
-                long directionStart = System.nanoTime();
+                List<Mat> matList = imageList.stream()
-                List<Mat> matList = new ArrayList<>(imageList.size());
+                        .map(image -> ImageUtils.toMat(image))
-                try {
+                        .collect(Collectors.toList());
-                    for (Image image : imageList) {
+                List<List<OcrItem>> ocrItemList = directionModel.batchDetect(boxeList, matList);
-                        matList.add(ImageUtils.toMat(image));
+                if (CollectionUtils.isEmpty(ocrItemList) || ocrItemList.size() != imageList.size()) {
-                    }
+                    throw new OcrException("方向检测失败");
-                    List<List<OcrItem>> ocrItemList = directionModel.batchDetect(effectiveBoxList, matList);
+                }
-                    log.debug("OCR流程-文本方向分类耗时={}ms, batchSize={}", elapsedMillis(directionStart), imageList.size());
+                allImageAlignList = new ArrayList<Image>();
-                    if (CollectionUtils.isEmpty(ocrItemList) || ocrItemList.size() != imageList.size()) {
+                for (int i = 0; i < ocrItemList.size(); i++) {
-                        throw new OcrException("方向检测失败");
+                    Mat srcMat = ImageUtils.toMat(imageList.get(i));
-                    }
+                    List<Image> imageAlignList = batchAlignWithDirection(ocrItemList.get(i), srcMat, manager);
-                    long alignStart = System.nanoTime();
+//                    for(int j = 0; j < imageAlignList.size(); j++){
-                    List<String> textList = new ArrayList<>();
+//                        ImageUtils.saveImage(imageAlignList.get(j),"dir-"+i+"-"+j+".png","/Users/xxx/Downloads/testing33");
-                    List<Image> chunkImages = new ArrayList<>(REC_CHUNK_SIZE);
+//                    }
-                    for (int i = 0; i < ocrItemList.size(); i++) {
+                    allImageAlignList.addAll(imageAlignList);
-                        Mat srcMat = matList.get(i);
-                        List<Image> imageAlignList = batchAlignWithDirection(ocrItemList.get(i), srcMat, manager);
-                        for (Image alignImage : imageAlignList) {
-                            chunkImages.add(alignImage);
-                            if (chunkImages.size() >= REC_CHUNK_SIZE) {
-                                textList.addAll(batchRecognizeChunk(predictor, chunkImages));
-                            }
-                        }
-                    }
-                    log.debug("OCR流程-方向矫正裁剪耗时={}ms, textBlocks={}", elapsedMillis(alignStart), textList.size() + chunkImages.size());
-                    long recStart = System.nanoTime();
-                    if (!chunkImages.isEmpty()) {
-                        textList.addAll(batchRecognizeChunk(predictor, chunkImages));
-                    }
-                    log.debug("OCR流程-识别模型调用耗时={}ms, textBlocks={}", elapsedMillis(recStart), textList.size());
-                    return buildOcrInfoList(effectiveBoxList, ocrRecOptions, manager, textList, imageList.size(), totalStart);
-                } finally {
-                    releaseTemporaryMats(imageList, matList);
                 }
             } else {
-                List<String> textList = new ArrayList<>();
+                for (int i = 0; i < boxeList.size(); i++) {
-                List<Image> chunkImages = new ArrayList<>(REC_CHUNK_SIZE);
+                    Mat srcMat = ImageUtils.toMat(imageList.get(i));
-                for (int i = 0; i < effectiveBoxList.size(); i++) {
+                    List<Image> imageAlignList = batchAlign(boxeList.get(i), srcMat, manager);
-                    Mat srcMat = null;
+//                    for(int j = 0; j < imageAlignList.size(); j++){
-                    try {
+//                        ImageUtils.saveImage(imageAlignList.get(j),i+"-"+j+".png","/Users/wenjie/Downloads/testing33");
-                        srcMat = ImageUtils.toMat(imageList.get(i));
+//                    }
-                        List<Image> imageAlignList = batchAlign(effectiveBoxList.get(i), srcMat, manager);
+                    allImageAlignList.addAll(imageAlignList);
-                        for (Image alignImage : imageAlignList) {
-                            chunkImages.add(alignImage);
-                            if (chunkImages.size() >= REC_CHUNK_SIZE) {
-                                textList.addAll(batchRecognizeChunk(predictor, chunkImages));
-                            }
-                        }
-                    } finally {
-                        releaseTemporaryMat(imageList.get(i), srcMat);
-                    }
                 }
-                long recStart = System.nanoTime();
-                if (!chunkImages.isEmpty()) {
-                    textList.addAll(batchRecognizeChunk(predictor, chunkImages));
-                }
-                log.debug("OCR流程-识别模型调用耗时={}ms, textBlocks={}", elapsedMillis(recStart), textList.size());
-                return buildOcrInfoList(effectiveBoxList, ocrRecOptions, manager, textList, imageList.size(), totalStart);
             }
+            List<String> textList = batchRecognize(allImageAlignList);
+            int textIndex = 0;
+            for (int i = 0; i < boxeList.size(); i++) {
+                List<RotatedBox> rotatedBoxes = new ArrayList<>();
+                for (int j = 0; j < boxeList.get(i).size(); j++) {
+                    if (textIndex >= textList.size()) {
+                        throw new OcrException("识别失败: 第" + i + "张图片, 第" + j + "个文本块，未识别到文本");
+                    }
+                    OcrBox box = boxeList.get(i).get(j);
+                    NDArray pointsArray = manager.create(box.toFloatArray());
+                    rotatedBoxes.add(new RotatedBox(pointsArray, textList.get(textIndex)));
+                    textIndex++;
+                }
+                OcrInfo ocrInfo = postProcessOcrResult(rotatedBoxes, ocrRecOptions);
+                ocrInfoList.add(ocrInfo);
+            }
+            return ocrInfoList;
         } catch (Exception e) {
             throw new OcrException("OCR检测错误", e);
         } finally {
@@ -472,41 +432,28 @@ public class OcrCommonRecModelImpl implements OcrCommonRecModel {
         }
     }
 
-    private List<OcrInfo> buildOcrInfoList(List<List<OcrBox>> effectiveBoxList,
+    private List<String> batchRecognize(List<Image> imageAlignList) {
-                                           OcrRecOptions ocrRecOptions,
+        Predictor<Image, String> predictor = null;
-                                           NDManager manager,
-                                           List<String> textList,
-                                           int batchSize,
-                                           long totalStart) {
-        int textIndex = 0;
-        List<OcrInfo> ocrInfoList = new ArrayList<>();
-        for (int i = 0; i < effectiveBoxList.size(); i++) {
-            List<RotatedBox> rotatedBoxes = new ArrayList<>();
-            for (int j = 0; j < effectiveBoxList.get(i).size(); j++) {
-                if (textIndex >= textList.size()) {
-                    throw new OcrException("识别失败: 第" + i + "张图片, 第" + j + "个文本块，未识别到文本");
-                }
-                OcrBox box = effectiveBoxList.get(i).get(j);
-                NDArray pointsArray = manager.create(box.toFloatArray());
-                rotatedBoxes.add(new RotatedBox(pointsArray, textList.get(textIndex)));
-                textIndex++;
-            }
-            OcrInfo ocrInfo = postProcessOcrResult(rotatedBoxes, ocrRecOptions);
-            ocrInfoList.add(ocrInfo);
-        }
-        log.debug("OCR流程总耗时={}ms, batchSize={}", elapsedMillis(totalStart), batchSize);
-        return ocrInfoList;
-    }
-
-    private List<String> batchRecognizeChunk(Predictor<Image, String> predictor, List<Image> imageAlignList) {
         try {
+            predictor = recPredictorPool.borrowObject();
             List<String> textList = predictor.batchPredict(imageAlignList);
+            imageAlignList.forEach(subImg -> ImageUtils.releaseOpenCVMat(subImg));
             return textList;
         } catch (Exception e) {
             throw new OcrException("OCR检测错误", e);
         } finally {
-            imageAlignList.forEach(ImageUtils::releaseOpenCVMat);
+            if (predictor != null) {
-            imageAlignList.clear();
+                try {
+                    recPredictorPool.returnObject(predictor); //归还
+                } catch (Exception e) {
+                    log.warn("归还Predictor失败", e);
+                    try {
+                        predictor.close(); // 归还失败才销毁
+                    } catch (Exception ex) {
+                        log.error("关闭Predictor失败", ex);
+                    }
+                }
+            }
         }
     }
 
@@ -578,24 +525,4 @@ public class OcrCommonRecModelImpl implements OcrCommonRecModel {
     public boolean isFromFactory() {
         return fromFactory;
     }
-
-    private long elapsedMillis(long startNanos) {
-        return (System.nanoTime() - startNanos) / 1_000_000;
-    }
-
-    private void releaseTemporaryMats(List<Image> imageList, List<Mat> matList) {
-        int size = Math.min(imageList.size(), matList.size());
-        for (int i = 0; i < size; i++) {
-            releaseTemporaryMat(imageList.get(i), matList.get(i));
-        }
-    }
-
-    private void releaseTemporaryMat(Image image, Mat mat) {
-        if (mat == null || image == null) {
-            return;
-        }
-        if (!(image.getWrappedImage() instanceof Mat)) {
-            mat.release();
-        }
-    }
 }

ocr/src/main/java/cn/smartjavaai/ocr/utils/BoxUtils.java

@@ -1,278 +0,0 @@
-package cn.smartjavaai.ocr.utils;
-
-import cn.smartjavaai.common.entity.Point;
-import cn.smartjavaai.ocr.entity.OcrBox;
-
-import java.util.ArrayList;
-import java.util.Comparator;
-import java.util.List;
-
-/**
- * @author dwj
- * @date 2026/1/11
- */
-public class BoxUtils {
-
-
-    public enum Direction {
-        UP, DOWN, LEFT, RIGHT
-    }
-
-    /**
-     * 寻找指定方向上距离最近的框
-     *
-     * @param anchor 锚点框，作为搜索的起始参考框
-     * @param boxList 候选框列表，在其中搜索目标框
-     * @param direction 搜索方向，可选 UP（上）、DOWN（下）、LEFT（左）、RIGHT（右）
-     * @return 找到的最近的 OcrBox，如果找不到符合条件的框则返回 null
-     * 
-     * @apiNote
-     * - 该方法会在指定方向上寻找与锚点框在同一行或同一列的最近邻框
-     * - 水平方向（LEFT/RIGHT）：要求候选框与锚点框在 Y 轴上有重叠（同一行）
-     * - 垂直方向（UP/DOWN）：要求候选框与锚点框在 X 轴上有重叠（同一列）
-     * - 使用欧几里得距离计算中心点之间的距离
-     */
-    public static OcrBox findNearestBox(OcrBox anchor, List<OcrBox> boxList, Direction direction) {
-        OcrBox nearest = null;
-        double bestScore = Double.MAX_VALUE;
-        AxisSystem axisSystem = buildAxisSystem(anchor);
-
-        for (OcrBox target : boxList) {
-            if (target == anchor) {
-                continue;
-            }
-
-            CandidateMetrics metrics = evaluateCandidate(anchor, target, direction, axisSystem);
-            if (metrics == null) {
-                continue;
-            }
-            if (metrics.score < bestScore) {
-                bestScore = metrics.score;
-                nearest = target;
-            }
-        }
-
-        return nearest;
-    }
-
-    /**
-     * 寻找指定方向上距离最近的多个框（按距离升序返回）
-     *
-     * @param anchor    锚点框
-     * @param boxList   候选框列表
-     * @param direction 搜索方向
-     * @param limit     返回的最大数量（<=0 时返回空列表）
-     * @return 按距离由近到远排序的 OcrBox 列表
-     */
-    public static List<OcrBox> findNearestBoxes(OcrBox anchor, List<OcrBox> boxList, Direction direction, int limit) {
-        if (anchor == null || boxList == null || boxList.isEmpty() || limit <= 0) {
-            return new ArrayList<>();
-        }
-
-        AxisSystem axisSystem = buildAxisSystem(anchor);
-        List<Neighbor> candidates = new ArrayList<>();
-
-        for (OcrBox target : boxList) {
-            if (target == anchor) {
-                continue;
-            }
-
-            CandidateMetrics metrics = evaluateCandidate(anchor, target, direction, axisSystem);
-            if (metrics == null) {
-                continue;
-            }
-            candidates.add(new Neighbor(target, metrics.score));
-        }
-
-        candidates.sort(Comparator.comparingDouble(n -> n.distance));
-
-        List<OcrBox> result = new ArrayList<>();
-        int size = Math.min(limit, candidates.size());
-        for (int i = 0; i < size; i++) {
-            result.add(candidates.get(i).box);
-        }
-        return result;
-    }
-
-    /**
-     * 内部使用的邻居结构体，存储框和距离
-     */
-    private static class Neighbor {
-        private final OcrBox box;
-        private final double distance;
-
-        private Neighbor(OcrBox box, double distance) {
-            this.box = box;
-            this.distance = distance;
-        }
-    }
-
-    private static CandidateMetrics evaluateCandidate(OcrBox anchor, OcrBox target, Direction direction, AxisSystem axisSystem) {
-        double mainAxisX = isHorizontalDirection(direction) ? axisSystem.horizontalAxisX : axisSystem.verticalAxisX;
-        double mainAxisY = isHorizontalDirection(direction) ? axisSystem.horizontalAxisY : axisSystem.verticalAxisY;
-        double crossAxisX = isHorizontalDirection(direction) ? axisSystem.verticalAxisX : axisSystem.horizontalAxisX;
-        double crossAxisY = isHorizontalDirection(direction) ? axisSystem.verticalAxisY : axisSystem.horizontalAxisY;
-
-        Projection anchorMain = projectBox(anchor, mainAxisX, mainAxisY);
-        Projection anchorCross = projectBox(anchor, crossAxisX, crossAxisY);
-        Projection targetMain = projectBox(target, mainAxisX, mainAxisY);
-        Projection targetCross = projectBox(target, crossAxisX, crossAxisY);
-        Point anchorCenter = getCenter(anchor);
-        Point targetCenter = getCenter(target);
-        double centerMainDelta = projectPointDelta(anchorCenter, targetCenter, mainAxisX, mainAxisY);
-
-        double mainGap;
-        switch (direction) {
-            case RIGHT:
-            case DOWN:
-                mainGap = targetMain.min - anchorMain.max;
-                if (centerMainDelta <= 0) {
-                    return null;
-                }
-                break;
-            case LEFT:
-            case UP:
-                mainGap = anchorMain.min - targetMain.max;
-                if (centerMainDelta >= 0) {
-                    return null;
-                }
-                break;
-            default:
-                return null;
-        }
-
-        double anchorMainSize = Math.max(1.0, anchorMain.max - anchorMain.min);
-        double targetMainSize = Math.max(1.0, targetMain.max - targetMain.min);
-        double allowedBacktrack = Math.min(anchorMainSize, targetMainSize) * 0.35;
-        if (mainGap < -allowedBacktrack) {
-            return null;
-        }
-
-        double overlap = Math.max(0.0, Math.min(anchorCross.max, targetCross.max) - Math.max(anchorCross.min, targetCross.min));
-        double minCrossSize = Math.max(1.0, Math.min(anchorCross.max - anchorCross.min, targetCross.max - targetCross.min));
-        double overlapRatio = overlap / minCrossSize;
-
-        double anchorCrossCenter = (anchorCross.min + anchorCross.max) / 2.0;
-        double targetCrossCenter = (targetCross.min + targetCross.max) / 2.0;
-        double crossCenterDistance = Math.abs(anchorCrossCenter - targetCrossCenter);
-        double crossTolerance = Math.max(anchorCross.max - anchorCross.min, targetCross.max - targetCross.min) * 0.6;
-
-        if (overlapRatio < 0.2 && crossCenterDistance > crossTolerance) {
-            return null;
-        }
-
-        double score = Math.max(0.0, mainGap) * 10.0 + crossCenterDistance + Math.abs(centerMainDelta) * 0.01;
-        if (overlapRatio < 0.2) {
-            score += (0.2 - overlapRatio) * 100.0;
-        }
-        return new CandidateMetrics(score);
-    }
-
-    private static boolean isHorizontalDirection(Direction direction) {
-        return direction == Direction.LEFT || direction == Direction.RIGHT;
-    }
-
-    private static Point getCenter(OcrBox box) {
-        float cx = (float) (box.getTopLeft().getX() + box.getTopRight().getX() + box.getBottomRight().getX() + box.getBottomLeft().getX()) / 4;
-        float cy = (float) (box.getTopLeft().getY() + box.getTopRight().getY() + box.getBottomRight().getY() + box.getBottomLeft().getY()) / 4;
-        return new Point(cx, cy);
-    }
-
-    private static AxisSystem buildAxisSystem(OcrBox anchor) {
-        Point topLeft = anchor.getTopLeft();
-        Point topRight = anchor.getTopRight();
-        Point bottomLeft = anchor.getBottomLeft();
-
-        double horizontalX = topRight.getX() - topLeft.getX();
-        double horizontalY = topRight.getY() - topLeft.getY();
-        double verticalX = bottomLeft.getX() - topLeft.getX();
-        double verticalY = bottomLeft.getY() - topLeft.getY();
-
-        double horizontalNorm = Math.hypot(horizontalX, horizontalY);
-        double verticalNorm = Math.hypot(verticalX, verticalY);
-
-        if (horizontalNorm < 1e-6) {
-            horizontalX = 1.0;
-            horizontalY = 0.0;
-            horizontalNorm = 1.0;
-        }
-        if (verticalNorm < 1e-6) {
-            verticalX = 0.0;
-            verticalY = 1.0;
-            verticalNorm = 1.0;
-        }
-
-        return new AxisSystem(
-                horizontalX / horizontalNorm,
-                horizontalY / horizontalNorm,
-                verticalX / verticalNorm,
-                verticalY / verticalNorm
-        );
-    }
-
-    private static Projection projectBox(OcrBox box, double axisX, double axisY) {
-        double[] values = new double[]{
-                dot(box.getTopLeft(), axisX, axisY),
-                dot(box.getTopRight(), axisX, axisY),
-                dot(box.getBottomRight(), axisX, axisY),
-                dot(box.getBottomLeft(), axisX, axisY)
-        };
-        double min = values[0];
-        double max = values[0];
-        for (int i = 1; i < values.length; i++) {
-            min = Math.min(min, values[i]);
-            max = Math.max(max, values[i]);
-        }
-        return new Projection(min, max);
-    }
-
-    private static double dot(Point point, double axisX, double axisY) {
-        return point.getX() * axisX + point.getY() * axisY;
-    }
-
-    private static double projectPointDelta(Point from, Point to, double axisX, double axisY) {
-        return (to.getX() - from.getX()) * axisX + (to.getY() - from.getY()) * axisY;
-    }
-
-    private static double min(double a, double b, double c, double d) {
-        return Math.min(Math.min(a, b), Math.min(c, d));
-    }
-
-    private static double max(double a, double b, double c, double d) {
-        return Math.max(Math.max(a, b), Math.max(c, d));
-    }
-
-    private static class AxisSystem {
-        private final double horizontalAxisX;
-        private final double horizontalAxisY;
-        private final double verticalAxisX;
-        private final double verticalAxisY;
-
-        private AxisSystem(double horizontalAxisX, double horizontalAxisY, double verticalAxisX, double verticalAxisY) {
-            this.horizontalAxisX = horizontalAxisX;
-            this.horizontalAxisY = horizontalAxisY;
-            this.verticalAxisX = verticalAxisX;
-            this.verticalAxisY = verticalAxisY;
-        }
-    }
-
-    private static class Projection {
-        private final double min;
-        private final double max;
-
-        private Projection(double min, double max) {
-            this.min = min;
-            this.max = max;
-        }
-    }
-
-    private static class CandidateMetrics {
-        private final double score;
-
-        private CandidateMetrics(double score) {
-            this.score = score;
-        }
-    }
-
-
-}

ocr/src/main/java/cn/smartjavaai/ocr/utils/OcrUtils.java

@@ -8,7 +8,6 @@ import ai.djl.modality.cv.util.NDImageUtils;
 import ai.djl.ndarray.NDArray;
 import ai.djl.ndarray.NDList;
 import ai.djl.ndarray.NDManager;
-import ai.djl.ndarray.types.Shape;
 import ai.djl.opencv.OpenCVImageFactory;
 import cn.smartjavaai.common.cv.SmartImageFactory;
 import cn.smartjavaai.common.entity.*;
@@ -413,42 +412,4 @@ public class OcrUtils {
     }
 
 
-    public static boolean isSameRow(NDArray box1, NDArray box2) {
-        // 1. 确保 box 是 (4, 2) 的形状
-        NDArray b1 = formatBox(box1);
-        NDArray b2 = formatBox(box2);
-
-        // 2. 获取 Y 坐标列（索引为 1 的列）
-        NDArray y1 = b1.get(":, 1");
-        NDArray y2 = b2.get(":, 1");
-
-        float yMin1 = y1.min().getFloat();
-        float yMax1 = y1.max().getFloat();
-        float yMin2 = y2.min().getFloat();
-        float yMax2 = y2.max().getFloat();
-
-        // 3. 计算重叠高度
-        float overlapHeight = Math.min(yMax1, yMax2) - Math.max(yMin1, yMin2);
-
-        if (overlapHeight <= 0) return false;
-
-        // 4. 计算各自高度
-        float h1 = yMax1 - yMin1;
-        float h2 = yMax2 - yMin2;
-
-        // 5. 判定标准
-        return overlapHeight > (Math.min(h1, h2) * 0.5f);
-    }
-
-    /**
-     * 辅助方法：将 1D 的 8个元素 转换为 2D 的 (4, 2)
-     */
-    private static NDArray formatBox(NDArray box) {
-        if (box.getShape().dimension() == 1) {
-            // 如果是 [x0, y0, x1, y1...] 这种 8 个元素的 1D 阵，转为 (4, 2)
-            return box.reshape(new Shape(4, 2));
-        }
-        return box;
-    }
-
 }

pom.xml

@@ -7,7 +7,7 @@
     <name>SmartJavaAI</name>
     <groupId>cn.smartjavaai</groupId>
     <artifactId>smartjavaai-parent</artifactId>
-    <version>1.1.2</version>
+    <version>1.1.1</version>
     <packaging>pom</packaging>
     <description>SmartJavaAI</description>
     <modules>
@@ -19,6 +19,7 @@
         <module>ocr</module>
         <module>bom</module>
         <module>speech</module>
+<!--        <module>face-pro</module>-->
     </modules>
 
     <properties>

speech/pom.xml

@@ -6,7 +6,7 @@
     <parent>
         <groupId>cn.smartjavaai</groupId>
         <artifactId>smartjavaai-parent</artifactId>
-        <version>1.1.2</version>
+        <version>1.1.1</version>
     </parent>
 
     <artifactId>speech</artifactId>
@@ -57,7 +57,7 @@
     </dependencies>
 
 
-    <version>1.1.2</version>
+    <version>1.1.1</version>
     <name>speech</name>
     <description>SmartJavaAI</description>
     <url>https://github.com/geekwenjie/SmartJavaAI</url>

translate/pom.xml

@@ -25,7 +25,7 @@
         </dependency>
     </dependencies>
 
-    <version>1.1.2</version>
+    <version>1.1.1</version>
     <name>translate</name>
     <description>SmartJavaAI</description>
     <url>https://github.com/geekwenjie/SmartJavaAI</url>

vision/pom.xml

@@ -6,11 +6,11 @@
     <parent>
         <groupId>cn.smartjavaai</groupId>
         <artifactId>smartjavaai-parent</artifactId>
-        <version>1.1.2</version>
+        <version>1.1.1</version>
     </parent>
 
     <artifactId>vision</artifactId>
-    <version>1.1.2</version>
+    <version>1.1.1</version>
     <name>vision</name>
     <description>SmartJavaAI</description>
     <url>https://github.com/geekwenjie/SmartJavaAI</url>