diff --git a/README_ch.md b/README_ch.md
index 2d543806ce2376188fd32656b75e759d6b611a3b..0c5d4e1c3e8237a0e3a96d6a581f8662e221e37a 100755
--- a/README_ch.md
+++ b/README_ch.md
@@ -78,7 +78,7 @@ PaddleOCR同时支持动态图与静态图两种编程范式
 - 算法介绍
     - [文本检测](./doc/doc_ch/algorithm_overview.md)
     - [文本识别](./doc/doc_ch/algorithm_overview.md)
-    - [PP-OCR Pipline](#PP-OCR)
+    - [PP-OCR Pipeline](#PP-OCR)
     - [端到端PGNet算法](./doc/doc_ch/pgnet.md)
 - 模型训练/评估
     - [文本检测](./doc/doc_ch/detection.md)
@@ -113,7 +113,7 @@ PaddleOCR同时支持动态图与静态图两种编程范式
 
 
 <a name="PP-OCR"></a>
-## PP-OCR Pipline
+## PP-OCR Pipeline
 <div align="center">
     <img src="./doc/ppocr_framework.png" width="800">
 </div>
diff --git a/deploy/cpp_infer/src/postprocess_op.cpp b/deploy/cpp_infer/src/postprocess_op.cpp
index bc794c8189abbec8c3bcb118cb8aca26dcf0c290..e7db70f3bff81390728c6b373b89cf06c74e4eca 100644
--- a/deploy/cpp_infer/src/postprocess_op.cpp
+++ b/deploy/cpp_infer/src/postprocess_op.cpp
@@ -186,18 +186,23 @@ float PostProcessor::PolygonScoreAcc(std::vector<cv::Point> contour,
   cv::Mat mask;
   mask = cv::Mat::zeros(ymax - ymin + 1, xmax - xmin + 1, CV_8UC1);
 
-  cv::Point rook_point[contour.size()];
+
+  cv::Point* rook_point = new cv::Point[contour.size()];
+   
   for (int i = 0; i < contour.size(); ++i) {
     rook_point[i] = cv::Point(int(box_x[i]) - xmin, int(box_y[i]) - ymin);
   }
   const cv::Point *ppt[1] = {rook_point};
   int npt[] = {int(contour.size())};
+
+
   cv::fillPoly(mask, ppt, npt, 1, cv::Scalar(1));
 
   cv::Mat croppedImg;
-  pred(cv::Rect(xmin, ymin, xmax - xmin + 1, ymax - ymin + 1))
-      .copyTo(croppedImg);
+  pred(cv::Rect(xmin, ymin, xmax - xmin + 1, ymax - ymin + 1)).copyTo(croppedImg);
   float score = cv::mean(croppedImg, mask)[0];
+
+  delete []rook_point;
   return score;
 }
 
diff --git a/tools/program.py b/tools/program.py
index 33bd35c23d0b6676edfb9c537e497eb68b828c4a..ad6fcbd9b5a1213e7e88ef7c82fde07ff29bcb80 100755
--- a/tools/program.py
+++ b/tools/program.py
@@ -197,9 +197,11 @@ def train(config,
         train_reader_cost = 0.0
         batch_sum = 0
         batch_start = time.time()
+        max_iter = len(train_dataloader) - 1 if platform.system(
+        ) == "Windows" else len(train_dataloader)
         for idx, batch in enumerate(train_dataloader):
             train_reader_cost += time.time() - batch_start
-            if idx >= len(train_dataloader):
+            if idx >= max_iter:
                 break
             lr = optimizer.get_lr()
             images = batch[0]