diff --git a/deploy/cpp_infer/readme.md b/deploy/cpp_infer/readme.md
index 8693bc79c0e70bb014616d005ec6d53848ab93d9..06561f7610f788d58481bea7bf7b8a9384fba2de 100644
--- a/deploy/cpp_infer/readme.md
+++ b/deploy/cpp_infer/readme.md
@@ -30,7 +30,7 @@ export root_path=$PWD
export install_path=${root_path}/opencv3
```
-然后在opencv源码路径下，按照下面的方式进行编译。
+* 然后在opencv源码路径下，按照下面的方式进行编译。
```shell
rm -rf build
@@ -59,7 +59,7 @@ make -j
make install
```
-`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PaddleClas代码编译。
+* `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PaddleClas代码编译。
以opencv3.4.7版本为例，最终在安装路径下的文件结构如下所示。**注意**：不同的opencv版本，下述的文件结构可能不同。
@@ -204,11 +204,11 @@ make -j
sh tools/run.sh
```
-
-最终屏幕上会输出检测结果如下。
+* 最终屏幕上会输出结果，如下图所示。
+
其中`class id`表示置信度最高的类别对应的id，score表示图片属于该类别的概率。
diff --git a/deploy/cpp_infer/readme_en.md b/deploy/cpp_infer/readme_en.md
new file mode 100644
index 0000000000000000000000000000000000000000..3c174cdd386c8d162aa9ac4948b8627b2630aba2
--- /dev/null
+++ b/deploy/cpp_infer/readme_en.md
@@ -0,0 +1,219 @@
+# Server-side C++ inference
+
+
+In this tutorial, we will introduce the detailed steps of deploying PaddleClas models on the server side.
+
+
+## 1. Prepare the environment
+
+### Environment
+
+- Linux, docker is recommended.
+- Windows, compilation based on `Visual Studio 2019 Community` is supported. In addition, you can refer to [How to use PaddleDetection to make a complete project](https://zhuanlan.zhihu.com/p/145446681) to compile by generating the `sln solution`.
+- This document mainly introduces the compilation and inference of PaddleClas C++ in Linux environment.
+- If you need to use the Inference Library in Windows environment, please refer to [The compilation tutorial in Windows](./docs/windows_vs2019_build.md) for detailed information.
+
+
+### 1.1 Compile opencv
+
+* 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 and uncompress command are as follows.
+
+```
+wget https://github.com/opencv/opencv/archive/3.4.7.tar.gz
+tar -xf 3.4.7.tar.gz
+```
+
+Finally, you can see the folder of `opencv-3.4.7/` in the current directory.
+
+* Compile opencv, the opencv source path (`root_path`) and installation path (`install_path`) should be set by yourself. Among them, `root_path` is the downloaded opencv source code path, and `install_path` is the installation path of opencv. In this case, the opencv source is `./opencv-3.4.7`.
+
+```shell
+cd ./opencv-3.4.7
+export root_path=$PWD
+export install_path=${root_path}/opencv3
+```
+
+* After entering the opencv source code path, you can compile it in the following way.
+
+
+```shell
+rm -rf build
+mkdir build
+cd build
+
+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
+```
+
+* After `make install` is completed, the opencv header file and library file will be generated in this folder for later PaddleClas source code compilation.
+
+Take opencv3.4.7 for example, the final file structure under the opencv installation path is as follows. **NOTICE**:The following file structure may be different for different Versions of Opencv.
+
+```
+opencv3/
+|-- bin
+|-- include
+|-- lib64
+|-- share
+```
+
+### 1.2 Compile or download the Paddle Inference Library
+
+* There are 2 ways to obtain the Paddle Inference Library, described in detail below.
+
+
+#### 1.2.1 Compile from the source code
+* If you want to get the latest Paddle Inference Library features, you can download the latest code from Paddle GitHub repository and compile the inference library from the source code.
+* You can refer to [Paddle Inference Library] (https://www.paddlepaddle.org.cn/documentation/docs/en/advanced_guide/inference_deployment/inference/build_and_install_lib_en.html) to get the Paddle source code from github, and then compile To generate the latest inference library. The method of using git to access the code is as follows.
+
+
+```shell
+git clone https://github.com/PaddlePaddle/Paddle.git
+```
+
+* After entering the Paddle directory, the compilation method is as follows.
+
+```shell
+rm -rf build
+mkdir build
+cd build
+
+cmake .. \
+ -DWITH_CONTRIB=OFF \
+ -DWITH_MKL=ON \
+ -DWITH_MKLDNN=ON \
+ -DWITH_TESTING=OFF \
+ -DCMAKE_BUILD_TYPE=Release \
+ -DWITH_INFERENCE_API_TEST=OFF \
+ -DON_INFER=ON \
+ -DWITH_PYTHON=ON
+make -j
+make inference_lib_dist
+```
+
+For more compilation parameter options, please refer to the official website of the Paddle C++ inference library:[https://www.paddlepaddle.org.cn/documentation/docs/en/advanced_guide/inference_deployment/inference/build_and_install_lib_en.html](https://www.paddlepaddle.org.cn/documentation/docs/en/advanced_guide/inference_deployment/inference/build_and_install_lib_en.html).
+
+
+* After the compilation process, you can see the following files in the folder of `build/fluid_inference_install_dir/`.
+
+```
+build/fluid_inference_install_dir/
+|-- CMakeCache.txt
+|-- paddle
+|-- third_party
+|-- version.txt
+```
+
+Among them, `paddle` is the Paddle library required for C++ prediction later, and `version.txt` contains the version information of the current inference library.
+
+
+
+#### 1.2.2 Direct download and installation
+
+* Different cuda versions of the Linux inference library (based on GCC 4.8.2) are provided on the
+[Paddle Inference Library official website](https://www.paddlepaddle.org.cn/documentation/docs/en/advanced_guide/inference_deployment/inference/build_and_install_lib_en.html). You can view and select the appropriate version of the inference library on the official website.
+
+
+* After downloading, use the following method to uncompress.
+
+```
+tar -xf fluid_inference.tgz
+```
+
+Finally you can see the following files in the folder of `fluid_inference/`.
+
+
+## 2. Compile and run the demo
+
+### 2.1 Export the inference model
+
+* You can refer to [Model inference]((../../tools/export_model.py))，export the inference model. After the model is exported, assuming it is placed in the `inference` directory, the directory structure is as follows.
+
+```
+inference/
+|--model
+|--params
+```
+
+**NOTICE**: Among them, `model` file stores the model structure information and the `params` file stores the model parameter information.Therefore, you could rename the files name exported by [Model inference]((../../tools/export_model.py)).
+
+### 2.2 Compile PaddleClas C++ inference demo
+
+
+* The compilation commands are as follows. The addresses of Paddle C++ inference library, opencv and other Dependencies need to be replaced with the actual addresses on your own machines.
+
+```shell
+sh tools/build.sh
+```
+
+Specifically, the content in `tools/build.sh` is as follows.
+
+```shell
+OPENCV_DIR=your_opencv_dir
+LIB_DIR=your_paddle_inference_dir
+CUDA_LIB_DIR=your_cuda_lib_dir
+CUDNN_LIB_DIR=your_cudnn_lib_dir
+
+BUILD_DIR=build
+rm -rf ${BUILD_DIR}
+mkdir ${BUILD_DIR}
+cd ${BUILD_DIR}
+cmake .. \
+ -DPADDLE_LIB=${LIB_DIR} \
+ -DWITH_MKL=ON \
+ -DDEMO_NAME=ocr_system \
+ -DWITH_GPU=OFF \
+ -DWITH_STATIC_LIB=OFF \
+ -DUSE_TENSORRT=OFF \
+ -DOPENCV_DIR=${OPENCV_DIR} \
+ -DCUDNN_LIB=${CUDNN_LIB_DIR} \
+ -DCUDA_LIB=${CUDA_LIB_DIR} \
+
+make -j
+```
+
+In the above parameters of command:
+
+* `OPENCV_DIR` is the opencv installation path;
+
+* `LIB_DIR` is the download (`fluid_inference` folder) or the generated Paddle Inference Library path (`build/fluid_inference_install_dir` folder);
+
+* `CUDA_LIB_DIR` is the cuda library file path, in docker; it is `/usr/local/cuda/lib64`;
+
+* `CUDNN_LIB_DIR` is the cudnn library file path, in docker it is `/usr/lib/x86_64-linux-gnu/`.
+
+After the compilation is completed, an executable file named `ocr_system` will be generated in the `build` folder.
+
+
+### Run the demo
+* Execute the following command to complete the classification of an image.
+
+```shell
+sh tools/run.sh
+```
+
+* The detection results will be shown on the screen, which is as follows.
+
+
+
+