update lite-deme readme, add prepare.sh

deploy/lite/prepare.sh

+#!/bin/bash
+
+mkdir -p  $1/demo/cxx/ocr/debug/
+cp  ../../ppocr/utils/ppocr_keys_v1.txt  $1/demo/cxx/ocr/debug/
+cp -r  ./*   $1/demo/cxx/ocr/
+cp ./config.txt  $1/demo/cxx/ocr/debug/
+cp ../../doc/imgs/11.jpg  $1/demo/cxx/ocr/debug/
+
+echo "Prepare Done"

deploy/lite/readme.md

@@ -168,17 +168,21 @@ wget  https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar && tar
     ```
 
  4. 准备优化后的模型、预测库文件、测试图像和使用的字典文件。
- 在预测库`inference_lite_lib.android.armv8/demo/cxx/`下新建一个`ocr/`文件夹，
- 将PaddleOCR repo中`PaddleOCR/deploy/lite/` 下的除`readme.md`所有文件放在新建的ocr文件夹下。在`ocr`文件夹下新建一个`debug`文件夹，
- 将C++预测库so文件复制到debug文件夹下。
-  ```
+ ```
+ git clone https://github.com/PaddlePaddle/PaddleOCR.git
+ cd PaddleOCR/deploy/lite/
+ # 运行prepare.sh，准备预测库文件、测试图像和使用的字典文件，并放置在预测库中的demo/cxx/ocr文件夹下
+ sh prepare.sh /{lite prediction library path}/inference_lite_lib.android.armv8
+
  # 进入OCR demo的工作目录
+ cd /{lite prediction library path}/inference_lite_lib.android.armv8/
  cd demo/cxx/ocr/
  # 将C++预测动态库so文件复制到debug文件夹中
  cp ../../../cxx/lib/libpaddle_light_api_shared.so ./debug/
-  ```
+ ```
+
  准备测试图像，以`PaddleOCR/doc/imgs/11.jpg`为例，将测试的图像复制到`demo/cxx/ocr/debug/`文件夹下。
- 准备字典文件，中文超轻量模型的字典文件是`PaddleOCR/ppocr/utils/ppocr_keys_v1.txt`，将其复制到`demo/cxx/ocr/debug/`文件夹下。
+ 准备lite opt工具优化后的模型文件，`ch_det_mv3_db_opt.nb，ch_rec_mv3_crnn_opt.nb`，放置在`demo/cxx/ocr/debug/`文件夹下。
 
  执行完成后，ocr文件夹下将有如下文件格式：
 

deploy/lite/readme_en.md

@@ -125,19 +125,49 @@ When the above code command is completed, there will be two more files `ch_det_m
 
     If there is `device` output, it means the installation was successful.
 
-4. Prepare optimized models, prediction library files, test images and dictionary files used. Create a new `ocr/` folder under the prediction library `inference_lite_lib.android.armv8/demo/cxx/`, and place all the files under `PaddleOCR/deploy/lite/` in the PaddleOCR repo except `readme.md` under the newly created ocr folder. Create a new debug folder under the ocr folder, and copy the C++ prediction library so file to the debug folder
+4. Prepare optimized models, prediction library files, test images and dictionary files used.
 
 ```
+ git clone https://github.com/PaddlePaddle/PaddleOCR.git
+ cd PaddleOCR/deploy/lite/
+ # run prepare.sh
+ sh prepare.sh /{lite prediction library path}/inference_lite_lib.android.armv8
 
-cd inference_lite_lib.android.armv8/demo/cxx/ocr/
-cp ../../../cxx/lib/libpaddle_light_api_shared.so ./debug/
+ #
+ cd /{lite prediction library path}/inference_lite_lib.android.armv8/
+ cd demo/cxx/ocr/
+ # copy paddle-lite C++ .so file to debug/ directory
+ cp ../../../cxx/lib/libpaddle_light_api_shared.so ./debug/
+
+ cd inference_lite_lib.android.armv8/demo/cxx/ocr/
+ cp ../../../cxx/lib/libpaddle_light_api_shared.so ./debug/
 
 ```
 
-Prepare the test image, taking `PaddleOCR/doc/imgs/11.jpg` as an example, copy the image file to the `demo/cxx/ocr/debug/` folder. The dictionary file for the Chinese super lightweight model is `PaddleOCR/ppocr/utils/ppocr_keys_v1.txt`, and copy it to the `demo/cxx/ocr/debug/` folder.
+Prepare the test image, taking `PaddleOCR/doc/imgs/11.jpg` as an example, copy the image file to the `demo/cxx/ocr/debug/` folder.
+Prepare the model files optimized by the lite opt tool, `ch_det_mv3_db_opt.nb, ch_rec_mv3_crnn_opt.nb`,
+and place them under the `demo/cxx/ocr/debug/` folder.
 
-After the execution is completed, the following file formats will be in the ocr folder:
 
+The structure of the OCR demo is as follows after the above command is executed:
+```
+demo/cxx/ocr/
+|-- debug/  
+|   |--ch_det_mv3_db_opt.nb             Detection model
+|   |--ch_rec_mv3_crnn_opt.nb           Recognition model
+|   |--11.jpg                           image for OCR
+|   |--ppocr_keys_v1.txt                Dictionary file
+|   |--libpaddle_light_api_shared.so    C++ .so file
+|   |--config.txt                       Config file
+|-- config.txt  
+|-- crnn_process.cc  
+|-- crnn_process.h
+|-- db_post_process.cc  
+|-- db_post_process.h
+|-- Makefile  
+|-- ocr_db_crnn.cc  
+
+```
 
 5. Run Model on phone