Merge pull request #4044 from Evezerest/2.3

Unified document title case

deploy/cpp_infer/readme.md

@@ -4,15 +4,32 @@
 C++在性能计算上优于python，因此，在大多数CPU、GPU部署场景，多采用C++的部署方式，本节将介绍如何在Linux\Windows (CPU\GPU)环境下配置C++环境并完成
 PaddleOCR模型部署。
 
+* [1. 准备环境](#1)
+  + [1.0 运行准备](#10)
+  + [1.1 编译opencv库](#11)
+  + [1.2 下载或者编译Paddle预测库](#12)
+    - [1.2.1 直接下载安装](#121)
+    - [1.2.2 预测库源码编译](#122)
+* [2 开始运行](#2)
+  + [2.1 将模型导出为inference model](#21)
+  + [2.2 编译PaddleOCR C++预测demo](#22)
+  + [2.3运行demo](#23)
+
+<a name="1"></a>
 
 ## 1. 准备环境
 
-### 运行准备
+<a name="10"></a>
+
+### 1.0 运行准备
+
 - Linux环境，推荐使用docker。
 - Windows环境，目前支持基于`Visual Studio 2019 Community`进行编译。
 
 * 该文档主要介绍基于Linux环境的PaddleOCR C++预测流程，如果需要在Windows下基于预测库进行C++预测，具体编译方法请参考[Windows下编译教程](./docs/windows_vs2019_build.md)
 
+<a name="11"></a>
+
 ### 1.1 编译opencv库
 
 * 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。
@@ -71,6 +88,8 @@ opencv3/
 |-- share
 ```
 
+<a name="12"></a>
+
 ### 1.2 下载或者编译Paddle预测库
 
 * 有2种方式获取Paddle预测库，下面进行详细介绍。
@@ -132,9 +151,12 @@ build/paddle_inference_install_dir/
 
 其中`paddle`就是C++预测所需的Paddle库，`version.txt`中包含当前预测库的版本信息。
 
+<a name="2"></a>
 
 ## 2 开始运行
 
+<a name="21"></a>
+
 ### 2.1 将模型导出为inference model
 
 * 可以参考[模型预测章节](../../doc/doc_ch/inference.md)，导出inference model，用于模型预测。模型导出之后，假设放在`inference`目录下，则目录结构如下。
@@ -149,6 +171,7 @@ inference/
 |   |--inference.pdmodel
 ```
 
+<a name="22"></a>
 
 ### 2.2 编译PaddleOCR C++预测demo
 
@@ -172,13 +195,14 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
 
 * 编译完成之后，会在`build`文件夹下生成一个名为`ppocr`的可执行文件。
 
+<a name="23"></a>
 
-### 运行demo
+### 2.3 运行demo
 
 运行方式：  
 ```shell
 ./build/ppocr <mode> [--param1] [--param2] [...]
-```  
+```
 其中，`mode`为必选参数，表示选择的功能，取值范围['det', 'rec', 'system']，分别表示调用检测、识别、检测识别串联（包括方向分类器）。具体命令如下：
 
 ##### 1. 只调用检测：
@@ -258,6 +282,4 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
 </div>
 
 
-### 2.3 注意
-
-* 在使用Paddle预测库时，推荐使用2.0.0版本的预测库。
+**注意：在使用Paddle预测库时，推荐使用2.0.0版本的预测库。**

deploy/cpp_infer/readme_en.md

@@ -1,4 +1,4 @@
-# Server-side C++ inference
+# Server-side C++ Inference
 
 This chapter introduces the C++ deployment method of the PaddleOCR model, and the corresponding python predictive deployment method refers to [document](../../doc/doc_ch/inference.md).
 C++ is better than python in terms of performance calculation. Therefore, in most CPU and GPU deployment scenarios, C++ deployment is mostly used.
@@ -6,14 +6,14 @@ This section will introduce how to configure the C++ environment and complete it
 PaddleOCR model deployment.
 
 
-## 1. Prepare the environment
+## 1. Prepare the Environment
 
 ### Environment
 
 - Linux, docker is recommended.
 
 
-### 1.1 Compile opencv
+### 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 command is as follows.
 
@@ -73,7 +73,7 @@ opencv3/
 |-- share
 ```
 
-### 1.2 Compile or download or  the Paddle inference library
+### 1.2 Compile or Download or the Paddle Inference Library
 
 * There are 2 ways to obtain the Paddle inference library, described in detail below.
 
@@ -136,7 +136,7 @@ build/paddle_inference_install_dir/
 Among them, `paddle` is the Paddle library required for C++ prediction later, and `version.txt` contains the version information of the current inference library.
 
 
-## 2. Compile and run the demo
+## 2. Compile and Run the Demo
 
 ### 2.1 Export the inference model
 
@@ -183,7 +183,7 @@ or the generated Paddle inference library path (`build/paddle_inference_install_
 Execute the built executable file:  
 ```shell
 ./build/ppocr <mode> [--param1] [--param2] [...]
-```  
+```
 Here, `mode` is a required parameter，and the value range is ['det', 'rec', 'system'], representing using detection only, using recognition only and using the end-to-end system respectively. Specifically,
 
 ##### 1. run det demo: