diff --git a/accuracy.txt b/accuracy.txt
index e4358fb..cb63832 100644
--- a/accuracy.txt
+++ b/accuracy.txt
@@ -1185,3 +1185,67 @@
 总图片数:204张,  未识出图片:6张,  定位率:97.0588%  
 平均字符差距:0.520202个,  完全匹配数:145张,  完全匹配率:73.2323%  
 总时间:176秒,  平均执行时间:0.862745秒
+2015-07-31 17:53:07
+总图片数:204张,  未识出图片:6张,  定位率:97.0588%  
+平均字符差距:0.520202个,  完全匹配数:145张,  完全匹配率:73.2323%  
+总时间:182秒,  平均执行时间:0.892157秒
+2015-07-31 18:02:12
+总图片数:204张,  未识出图片:6张,  定位率:97.0588%  
+平均字符差距:0.520202个,  完全匹配数:145张,  完全匹配率:73.2323%  
+总时间:183秒,  平均执行时间:0.897059秒
+2015-07-31 18:06:37
+总图片数:204张,  未识出图片:6张,  定位率:97.0588%  
+平均字符差距:0.520202个,  完全匹配数:145张,  完全匹配率:73.2323%  
+总时间:182秒,  平均执行时间:0.892157秒
+2015-07-31 18:11:16
+总图片数:1张,  未识出图片:0张,  定位率:100%  
+平均字符差距:0个,  完全匹配数:1张,  完全匹配率:100%  
+总时间:1秒,  平均执行时间:1秒
+2015-07-31 18:11:31
+总图片数:1张,  未识出图片:0张,  定位率:100%  
+平均字符差距:0个,  完全匹配数:1张,  完全匹配率:100%  
+总时间:0秒,  平均执行时间:0秒
+2015-07-31 18:18:20
+总图片数:1张,  未识出图片:0张,  定位率:100%  
+平均字符差距:0个,  完全匹配数:1张,  完全匹配率:100%  
+总时间:0秒,  平均执行时间:0秒
+2015-07-31 18:21:34
+总图片数:204张,  未识出图片:6张,  定位率:97.0588%  
+平均字符差距:0.510101个,  完全匹配数:147张,  完全匹配率:74.2424%  
+总时间:182秒,  平均执行时间:0.892157秒
+2015-07-31 18:32:25
+总图片数:204张,  未识出图片:6张,  定位率:97.0588%  
+平均字符差距:1.05556个,  完全匹配数:108张,  完全匹配率:54.5455%  
+总时间:214秒,  平均执行时间:1.04902秒
+2015-07-31 18:36:36
+总图片数:204张,  未识出图片:6张,  定位率:97.0588%  
+平均字符差距:0.510101个,  完全匹配数:147张,  完全匹配率:74.2424%  
+总时间:183秒,  平均执行时间:0.897059秒
+2015-07-31 18:41:32
+总图片数:1张,  未识出图片:0张,  定位率:100%  
+平均字符差距:0个,  完全匹配数:1张,  完全匹配率:100%  
+总时间:0秒,  平均执行时间:0秒
+2015-07-31 18:44:39
+总图片数:204张,  未识出图片:6张,  定位率:97.0588%  
+平均字符差距:0.959596个,  完全匹配数:115张,  完全匹配率:58.0808%  
+总时间:184秒,  平均执行时间:0.901961秒
+2015-07-31 18:50:08
+总图片数:204张,  未识出图片:6张,  定位率:97.0588%  
+平均字符差距:0.914141个,  完全匹配数:115张,  完全匹配率:58.0808%  
+总时间:185秒,  平均执行时间:0.906863秒
+2015-07-31 18:53:53
+总图片数:204张,  未识出图片:6张,  定位率:97.0588%  
+平均字符差距:0.520202个,  完全匹配数:145张,  完全匹配率:73.2323%  
+总时间:186秒,  平均执行时间:0.911765秒
+2015-07-31 19:01:29
+总图片数:204张,  未识出图片:6张,  定位率:97.0588%  
+平均字符差距:1.01515个,  完全匹配数:111张,  完全匹配率:56.0606%  
+总时间:187秒,  平均执行时间:0.916667秒
+2015-07-31 19:09:06
+总图片数:204张,  未识出图片:6张,  定位率:97.0588%  
+平均字符差距:0.535354个,  完全匹配数:146张,  完全匹配率:73.7374%  
+总时间:202秒,  平均执行时间:0.990196秒
+2015-07-31 19:13:50
+总图片数:204张,  未识出图片:6张,  定位率:97.0588%  
+平均字符差距:0.520202个,  完全匹配数:145张,  完全匹配率:73.2323%  
+总时间:215秒,  平均执行时间:1.05392秒
diff --git a/include/easypr/chars_recognise.h b/include/easypr/chars_recognise.h
index 1769008..2ac6aad 100644
--- a/include/easypr/chars_recognise.h
+++ b/include/easypr/chars_recognise.h
@@ -27,7 +27,7 @@ class CCharsRecognise {
   ~CCharsRecognise();
 
   //! 字符分割与识别
-  int charsRecognise(Mat, String&);
+  int charsRecognise(Mat, String&, int index = 0);
 
   string charsRecognise(Mat plate);
 
diff --git a/include/easypr/chars_segment.h b/include/easypr/chars_segment.h
index f4b51c9..cfcd9f0 100644
--- a/include/easypr/chars_segment.h
+++ b/include/easypr/chars_segment.h
@@ -24,7 +24,7 @@ class CCharsSegment {
   CCharsSegment();
 
   //! 字符分割
-  int charsSegment(Mat, vector<Mat>&);
+  int charsSegment(Mat, vector<Mat>&, int index = 0);
 
   //! 字符尺寸验证
   bool verifyCharSizes(Mat r);
diff --git a/include/easypr/plate_recognize.h b/include/easypr/plate_recognize.h
index 84978cb..5ffc20d 100644
--- a/include/easypr/plate_recognize.h
+++ b/include/easypr/plate_recognize.h
@@ -25,7 +25,7 @@ class CPlateRecognize : public CPlateDetect, public CCharsRecognise {
   CPlateRecognize();
 
   //! 车牌检测与字符识别
-  int plateRecognize(cv::Mat src, std::vector<std::string>& licenseVec);
+  int plateRecognize(cv::Mat src, std::vector<std::string>& licenseVec, int index = 0);
 
   //! 生活模式与工业模式切换
   inline void setLifemode(bool param) { CPlateDetect::setPDLifemode(param); }
diff --git a/src/core/chars_recognise.cpp b/src/core/chars_recognise.cpp
index a327778..e9f349c 100644
--- a/src/core/chars_recognise.cpp
+++ b/src/core/chars_recognise.cpp
@@ -23,13 +23,14 @@ void CCharsRecognise::LoadANN(string s) {
 string CCharsRecognise::charsRecognise(Mat plate) {
   return m_charsIdentify->charsIdentify(plate);
 }
-int CCharsRecognise::charsRecognise(Mat plate, string& plateLicense) {
+
+int CCharsRecognise::charsRecognise(Mat plate, string& plateLicense, int index) {
   //车牌字符方块集合
   vector<Mat> matVec;
 
   string plateIdentify = "";
 
-  int result = m_charsSegment->charsSegment(plate, matVec);
+  int result = m_charsSegment->charsSegment(plate, matVec, index);
   if (result == 0) {
     int num = matVec.size();
     for (int j = 0; j < num; j++) {
diff --git a/src/core/chars_segment.cpp b/src/core/chars_segment.cpp
index 8267f85..3b79c6a 100644
--- a/src/core/chars_segment.cpp
+++ b/src/core/chars_segment.cpp
@@ -73,7 +73,7 @@ Mat CCharsSegment::preprocessChar(Mat in) {
 
 
 //! 字符分割与排序
-int CCharsSegment::charsSegment(Mat input, vector<Mat>& resultVec) {
+int CCharsSegment::charsSegment(Mat input, vector<Mat>& resultVec, int index) {
 
   if (!input.data) return 0x01;
 
@@ -135,7 +135,7 @@ int CCharsSegment::charsSegment(Mat input, vector<Mat>& resultVec) {
 
   if (m_debug) {
     stringstream ss(stringstream::in | stringstream::out);
-    ss << "resources/image/tmp/debug_char_threshold"  << ".jpg";
+    ss << "resources/image/tmp/debug_char_threshold_" << index << ".jpg";
     utils::imwrite(ss.str(), img_threshold);
   }
 
@@ -147,7 +147,7 @@ int CCharsSegment::charsSegment(Mat input, vector<Mat>& resultVec) {
 
   if (m_debug) {
     stringstream ss(stringstream::in | stringstream::out);
-    ss << "resources/image/tmp/debug_char_clearLiuDing" << ".jpg";
+    ss << "resources/image/tmp/debug_char_clearLiuDing_" << index << ".jpg";
     utils::imwrite(ss.str(), img_threshold);
   }
 
@@ -184,22 +184,22 @@ int CCharsSegment::charsSegment(Mat input, vector<Mat>& resultVec) {
 
   size_t specIndex = 0;
 
-  //获得特殊字符对应的Rectt,如苏A的"A"
+  // 获得特殊字符对应的Rectt,如苏A的"A"
   specIndex = GetSpecificRect(sortedRect);
 
   if (m_debug) {
     if (specIndex < sortedRect.size()) {
       Mat specMat(img_threshold, sortedRect[specIndex]);
       stringstream ss(stringstream::in | stringstream::out);
-      ss << "resources/image/tmp/debug_specMat"
+      ss << "resources/image/tmp/debug_specMat_" << index
          << ".jpg";
       utils::imwrite(ss.str(), specMat);
     }
   }
 
-  //根据特定Rect向左反推出中文字符
-  //这样做的主要原因是根据findContours方法很难捕捉到中文字符的准确Rect，因此仅能
-  //退过特定算法来指定
+  // 根据特定Rect向左反推出中文字符
+  // 这样做的主要原因是根据findContours方法很难捕捉到中文字符的准确Rect，因此仅能
+  // 退过特定算法来指定
   Rect chineseRect;
   if (specIndex < sortedRect.size())
     chineseRect = GetChineseRect(sortedRect[specIndex]);
@@ -209,7 +209,7 @@ int CCharsSegment::charsSegment(Mat input, vector<Mat>& resultVec) {
   if (m_debug) {
     Mat chineseMat(img_threshold, chineseRect);
     stringstream ss(stringstream::in | stringstream::out);
-    ss << "resources/image/tmp/debug_chineseMat"
+    ss << "resources/image/tmp/debug_chineseMat_" << index
        << ".jpg";
     utils::imwrite(ss.str(), chineseMat);
   }
@@ -223,17 +223,49 @@ int CCharsSegment::charsSegment(Mat input, vector<Mat>& resultVec) {
 
   if (newSortedRect.size() == 0) return 0x05;
 
+  // 开始截取每个字符
   for (size_t i = 0; i < newSortedRect.size(); i++) {
     Rect mr = newSortedRect[i];
+
     Mat auxRoi(img_threshold, mr);
 
+    // 使用灰度图来截取图块，然后依次对每个图块进行大津阈值来二值化
+    //Mat auxRoi(input_grey, mr);
+    //Mat newRoi;
+
+    //if (BLUE == plateType) {
+
+    // /* img_threshold = auxRoi.clone();
+    //  int w = input_grey.cols;
+    //  int h = input_grey.rows;
+    //  Mat tmp = input_grey(Rect_<double>(w * 0.1, h * 0.1, w * 0.8, h * 0.8));
+    //  int threadHoldV = ThresholdOtsu(tmp);*/
+
+    //  threshold(auxRoi, newRoi, 5, 255, CV_THRESH_BINARY + CV_THRESH_OTSU);
+    //}
+    //else if (YELLOW == plateType) {
+    //  threshold(auxRoi, newRoi, 5, 255, CV_THRESH_BINARY_INV + CV_THRESH_OTSU);
+
+    //}
+    //else if (WHITE == plateType) {
+    //  threshold(auxRoi, newRoi, 5, 255, CV_THRESH_OTSU + CV_THRESH_BINARY_INV);
+    //}
+    //else {
+    //  threshold(auxRoi, newRoi, 5, 255, CV_THRESH_OTSU + CV_THRESH_BINARY);
+    //}
+
+    // 归一化大小
     auxRoi = preprocessChar(auxRoi);
-    if (m_debug) {
+
+    // 假设我们要重新训练ANN模型，在这里需要把训练样板输出
+    if (i == 0) {
       stringstream ss(stringstream::in | stringstream::out);
-      ss << "resources/image/tmp/debug_char_auxRoi_" << (i)
+      ss << "resources/image/tmp/debug_char_auxRoi_" << index << "_" << (i)
         << ".jpg";
       utils::imwrite(ss.str(), auxRoi);
     }
+
+    // 每个字符图块输入到下面的步骤进行处理
     resultVec.push_back(auxRoi);
   }
 
diff --git a/src/core/plate_recognize.cpp b/src/core/plate_recognize.cpp
index 9980f01..23b746b 100644
--- a/src/core/plate_recognize.cpp
+++ b/src/core/plate_recognize.cpp
@@ -12,16 +12,16 @@ CPlateRecognize::CPlateRecognize() {
 }
 
 // !车牌识别模块
-int CPlateRecognize::plateRecognize(Mat src, std::vector<string> &licenseVec) {
+int CPlateRecognize::plateRecognize(Mat src, std::vector<string> &licenseVec, int index) {
   // 车牌方块集合
   vector<CPlate> plateVec;
 
   // 进行深度定位，使用颜色信息与二次Sobel
-  int resultPD = plateDetect(src, plateVec, getPDDebug(), 0);
+  int resultPD = plateDetect(src, plateVec, getPDDebug(), index);
 
   if (resultPD == 0) {
     int num = plateVec.size();
-    int index = 0;
+    int i = 0;
 
     //依次识别每个车牌内的符号
     for (int j = 0; j < num; j++) {
@@ -33,7 +33,7 @@ int CPlateRecognize::plateRecognize(Mat src, std::vector<string> &licenseVec) {
 
       //获取车牌号
       string plateIdentify = "";
-      int resultCR = charsRecognise(plate, plateIdentify);
+      int resultCR = charsRecognise(plate, plateIdentify, index);
       if (resultCR == 0) {
         string license = plateType + ":" + plateIdentify;
         licenseVec.push_back(license);
@@ -52,11 +52,11 @@ int CPlateRecognize::plateRecognize(Mat src, std::vector<string> &licenseVec) {
 
         int height = 36;
         int width = 136;
-        if (height * index + height < result.rows) {
-          Mat imageRoi = result(Rect(0, 0 + height * index, width, height));
+        if (height * i + height < result.rows) {
+          Mat imageRoi = result(Rect(0, 0 + height * i, width, height));
           addWeighted(imageRoi, 0, plate, 1, 0, imageRoi);
         }
-        index++;
+        i++;
 
         RotatedRect minRect = item.getPlatePos();
         Point2f rect_points[4];
diff --git a/src/train/ann_train.cpp b/src/train/ann_train.cpp
index 75cc0b7..39a81ab 100644
--- a/src/train/ann_train.cpp
+++ b/src/train/ann_train.cpp
@@ -208,6 +208,7 @@ void saveModel(int _predictsize, int _neurons) {
   cout << "End the saveModelChar" << endl;
 
   string model_name = "resources/train/ann.xml";
+
   // if(1)
   //{
   //	stringstream ss(stringstream::in | stringstream::out);
diff --git a/test/accuracy.hpp b/test/accuracy.hpp
index cc47ec1..775ff4a 100644
--- a/test/accuracy.hpp
+++ b/test/accuracy.hpp
@@ -72,7 +72,7 @@ int accuracyTest(const char* test_path) {
     cout << "原牌:" << plateLicense << endl;
 
     vector<string> plateVec;
-    int result = pr.plateRecognize(src, plateVec);
+    int result = pr.plateRecognize(src, plateVec, i);
     if (result == 0) {
       int num = plateVec.size();
 
diff --git a/test/main.cpp b/test/main.cpp
index 940c6b4..20347de 100644
--- a/test/main.cpp
+++ b/test/main.cpp
@@ -5,6 +5,9 @@
 #include "chars.hpp"
 #include "plate.hpp"
 
+
+extern int annMain();
+
 namespace easypr {
 
 namespace demo {
@@ -421,7 +424,7 @@ int main(int argc, const char* argv[]) {
           std::cout << "Run \"easypr_test svm\" for more usage." << std::endl;
           break;
         case 4:
-          // TODO
+          annMain();
           break;
         case 5:
           easypr::preprocess::generate_gdts();