diff --git a/deploy/cpp_infer/readme.md b/deploy/cpp_infer/readme.md
index f88d021d0a050aeecf859981cc2de1cee8f3a2c0..92ef70b642dc1eaed9e694b1ae756f76ee548703 100644
--- a/deploy/cpp_infer/readme.md
+++ b/deploy/cpp_infer/readme.md
@@ -34,10 +34,10 @@ PaddleOCR模型部署。
 
 * 首先需要从opencv官网上下载在Linux环境下源码编译的包，以opencv3.4.7为例，下载命令如下。
 
-```
+```bash
 cd deploy/cpp_infer
-wget https://github.com/opencv/opencv/archive/3.4.7.tar.gz
-tar -xf 3.4.7.tar.gz
+wget https://paddleocr.bj.bcebos.com/libs/opencv/opencv-3.4.7.tar.gz
+tar -xf opencv-3.4.7.tar.gz
 ```
 
 最终可以在当前目录下看到`opencv-3.4.7/`的文件夹。
@@ -45,12 +45,13 @@ tar -xf 3.4.7.tar.gz
 * 编译opencv，设置opencv源码路径(`root_path`)以及安装路径(`install_path`)。进入opencv源码路径下，按照下面的方式进行编译。
 
 ```shell
-root_path=your_opencv_root_path
+root_path="your_opencv_root_path"
 install_path=${root_path}/opencv3
+build_dir=${root_path}/build
 
-rm -rf build
-mkdir build
-cd build
+rm -rf ${build_dir}
+mkdir ${build_dir}
+cd ${build_dir}
 
 cmake .. \
     -DCMAKE_INSTALL_PREFIX=${install_path} \
@@ -74,6 +75,11 @@ make -j
 make install
 ```
 
+也可以直接修改`tools/build_opencv.sh`的内容，然后直接运行下面的命令进行编译。
+
+```shell
+sh tools/build_opencv.sh
+```
 
 其中`root_path`为下载的opencv源码路径，`install_path`为opencv的安装路径，`make install`完成之后，会在该文件夹下生成opencv头文件和库文件，用于后面的OCR代码编译。
 
@@ -233,12 +239,12 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
     --image_dir=../../doc/imgs/12.jpg
 ```
 
-更多参数如下：
+更多支持的可调节参数解释如下：
 
 - 通用参数
 
 |参数名称|类型|默认参数|意义|
-| --- | --- | --- | --- |
+| :---: | :---: | :---: | :---: |
 |use_gpu|bool|false|是否使用GPU|
 |gpu_id|int|0|GPU id，使用GPU时有效|
 |gpu_mem|int|4000|申请的GPU内存|
@@ -248,7 +254,7 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
 - 检测模型相关
 
 |参数名称|类型|默认参数|意义|
-| --- | --- | --- | --- |
+| :---: | :---: | :---: | :---: |
 |det_model_dir|string|-|检测模型inference model地址|
 |max_side_len|int|960|输入图像长宽大于960时，等比例缩放图像，使得图像最长边为960|
 |det_db_thresh|float|0.3|用于过滤DB预测的二值化图像，设置为0.-0.3对结果影响不明显|
@@ -260,7 +266,7 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
 - 方向分类器相关
 
 |参数名称|类型|默认参数|意义|
-| --- | --- | --- | --- |
+| :---: | :---: | :---: | :---: |
 |use_angle_cls|bool|false|是否使用方向分类器|
 |cls_model_dir|string|-|方向分类器inference model地址|
 |cls_thresh|float|0.9|方向分类器的得分阈值|
@@ -268,7 +274,7 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
 - 识别模型相关
 
 |参数名称|类型|默认参数|意义|
-| --- | --- | --- | --- |
+| :---: | :---: | :---: | :---: |
 |rec_model_dir|string|-|识别模型inference model地址|
 |char_list_file|string|../../ppocr/utils/ppocr_keys_v1.txt|字典文件|
 
diff --git a/deploy/cpp_infer/readme_en.md b/deploy/cpp_infer/readme_en.md
index 48de51ae726e662f48d465b8489a494448dafac1..fd6d953de1f9168da734d6c5eda945c670cfce37 100644
--- a/deploy/cpp_infer/readme_en.md
+++ b/deploy/cpp_infer/readme_en.md
@@ -17,10 +17,10 @@ PaddleOCR model deployment.
 
 * First of all, you need to download the source code compiled package in the Linux environment from the opencv official website. Taking opencv3.4.7 as an example, the download command is as follows.
 
-```
+```bash
 cd deploy/cpp_infer
-wget https://github.com/opencv/opencv/archive/3.4.7.tar.gz
-tar -xf 3.4.7.tar.gz
+wget https://paddleocr.bj.bcebos.com/libs/opencv/opencv-3.4.7.tar.gz
+tar -xf opencv-3.4.7.tar.gz
 ```
 
 Finally, you can see the folder of `opencv-3.4.7/` in the current directory.
diff --git a/deploy/cpp_infer/tools/build_opencv.sh b/deploy/cpp_infer/tools/build_opencv.sh
new file mode 100644
index 0000000000000000000000000000000000000000..9f5d556c1101023fd5bef15eab505b673b25e6ee
--- /dev/null
+++ b/deploy/cpp_infer/tools/build_opencv.sh
@@ -0,0 +1,28 @@
+root_path="/paddle/PaddleOCR/deploy/cpp_infer/opencv-3.4.7"
+install_path=${root_path}/opencv3
+build_dir=${root_path}/build
+
+rm -rf ${build_dir}
+mkdir ${build_dir}
+cd ${build_dir}
+
+cmake .. \
+    -DCMAKE_INSTALL_PREFIX=${install_path} \
+    -DCMAKE_BUILD_TYPE=Release \
+    -DBUILD_SHARED_LIBS=OFF \
+    -DWITH_IPP=OFF \
+    -DBUILD_IPP_IW=OFF \
+    -DWITH_LAPACK=OFF \
+    -DWITH_EIGEN=OFF \
+    -DCMAKE_INSTALL_LIBDIR=lib64 \
+    -DWITH_ZLIB=ON \
+    -DBUILD_ZLIB=ON \
+    -DWITH_JPEG=ON \
+    -DBUILD_JPEG=ON \
+    -DWITH_PNG=ON \
+    -DBUILD_PNG=ON \
+    -DWITH_TIFF=ON \
+    -DBUILD_TIFF=ON
+
+make -j
+make install
diff --git a/tools/infer/predict_e2e.py b/tools/infer/predict_e2e.py
index 08b87f36b0670e98e54c3f38c9328fe9462a6d0f..c00d101aa601c05230da39bc19f0e5068bc80aa2 100755
--- a/tools/infer/predict_e2e.py
+++ b/tools/infer/predict_e2e.py
@@ -68,7 +68,6 @@ class TextE2E(object):
             postprocess_params["character_dict_path"] = args.e2e_char_dict_path
             postprocess_params["valid_set"] = args.e2e_pgnet_valid_set
             postprocess_params["mode"] = args.e2e_pgnet_mode
-            self.e2e_pgnet_polygon = args.e2e_pgnet_polygon
         else:
             logger.info("unknown e2e_algorithm:{}".format(self.e2e_algorithm))
             sys.exit(0)
diff --git a/tools/infer/utility.py b/tools/infer/utility.py
index 85f68d9bdb4f15ec80a470af6b132ebda33398a3..eeccc90989ab44a12ea69cb2c4fe948b5de5e307 100755
--- a/tools/infer/utility.py
+++ b/tools/infer/utility.py
@@ -96,7 +96,6 @@ def init_args():
     parser.add_argument(
         "--e2e_char_dict_path", type=str, default="./ppocr/utils/ic15_dict.txt")
     parser.add_argument("--e2e_pgnet_valid_set", type=str, default='totaltext')
-    parser.add_argument("--e2e_pgnet_polygon", type=str2bool, default=True)
     parser.add_argument("--e2e_pgnet_mode", type=str, default='fast')
 
     # params for text classifier