littletomatodonkey
GitHub
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63fe8128d3
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@ -184,7 +184,7 @@ make -j
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### 运行demo
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* 执行以下命令，完成对一幅图像的OCR识别与检测，最终输出
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* 执行以下命令，完成对一幅图像的OCR识别与检测。
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```shell
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sh tools/run.sh
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@ -0,0 +1,213 @@
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# Server-side C++ inference
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In this tutorial, we will introduce the detailed steps of deploying PaddleOCR ultra-lightweight Chinese detection and recognition models on the server side.
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## 1. Prepare the environment
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### Environment
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- Linux, docker is recommended.
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### 1.1 Compile opencv
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* 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.
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```
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wget https://github.com/opencv/opencv/archive/3.4.7.tar.gz
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tar -xf 3.4.7.tar.gz
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```
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Finally, you can see the folder of `opencv-3.4.7/` in the current directory.
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* Compile opencv, the opencv source path (`root_path`) and installation path (`install_path`) should be set by yourself. Enter the opencv source code path and compile it in the following way.
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```shell
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root_path=your_opencv_root_path
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install_path=${root_path}/opencv3
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rm -rf build
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mkdir build
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cd build
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cmake .. \
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-DCMAKE_INSTALL_PREFIX=${install_path} \
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-DCMAKE_BUILD_TYPE=Release \
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-DBUILD_SHARED_LIBS=OFF \
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-DWITH_IPP=OFF \
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-DBUILD_IPP_IW=OFF \
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-DWITH_LAPACK=OFF \
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-DWITH_EIGEN=OFF \
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-DCMAKE_INSTALL_LIBDIR=lib64 \
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-DWITH_ZLIB=ON \
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-DBUILD_ZLIB=ON \
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-DWITH_JPEG=ON \
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-DBUILD_JPEG=ON \
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-DWITH_PNG=ON \
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-DBUILD_PNG=ON \
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-DWITH_TIFF=ON \
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-DBUILD_TIFF=ON
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make -j
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make install
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```
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Among them, `root_path` is the downloaded opencv source code path, and `install_path` is the installation path of opencv. After `make install` is completed, the opencv header file and library file will be generated in this folder for later OCR source code compilation.
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The final file structure under the opencv installation path is as follows.
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```
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opencv3/
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|-- bin
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|-- include
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|-- lib
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|-- lib64
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|-- share
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```
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### 1.2 Compile or download or the Paddle inference library
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* There are 2 ways to obtain the Paddle inference library, described in detail below.
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#### 1.2.1 Compile from the source code
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* 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.
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* 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.
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```shell
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git clone https://github.com/PaddlePaddle/Paddle.git
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```
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* After entering the Paddle directory, the compilation method is as follows.
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```shell
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rm -rf build
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mkdir build
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cd build
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cmake .. \
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-DWITH_CONTRIB=OFF \
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-DWITH_MKL=ON \
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-DWITH_MKLDNN=ON \
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-DWITH_TESTING=OFF \
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-DCMAKE_BUILD_TYPE=Release \
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-DWITH_INFERENCE_API_TEST=OFF \
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-DON_INFER=ON \
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-DWITH_PYTHON=ON
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make -j
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make inference_lib_dist
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```
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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).
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* After the compilation process, you can see the following files in the folder of `build/fluid_inference_install_dir/`.
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```
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build/fluid_inference_install_dir/
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|-- CMakeCache.txt
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|-- paddle
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|-- third_party
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|-- version.txt
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```
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Among them, `paddle` is the Paddle library required for C++ prediction later, and `version.txt` contains the version information of the current inference library.
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#### 1.2.2 Direct download and installation
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* Different cuda versions of the Linux inference library (based on GCC 4.8.2) are provided on the
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[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.
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* After downloading, use the following method to uncompress.
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```
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tar -xf fluid_inference.tgz
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```
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Finally you can see the following files in the folder of `fluid_inference/`.
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## 2. Compile and run the demo
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### 2.1 Export the inference model
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* You can refer to [Model inference](../../doc/doc_ch/inference.md)，export the inference model. After the model is exported, assuming it is placed in the `inference` directory, the directory structure is as follows.
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```
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inference/
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|-- det_db
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| |--model
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| |--params
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|-- rec_rcnn
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| |--model
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| |--params
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```
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### 2.2 Compile PaddleOCR C++ inference demo
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* 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.
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```shell
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sh tools/build.sh
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```
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具体地，`tools/build.sh`中内容如下。
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```shell
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OPENCV_DIR=your_opencv_dir
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LIB_DIR=your_paddle_inference_dir
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CUDA_LIB_DIR=your_cuda_lib_dir
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CUDNN_LIB_DIR=your_cudnn_lib_dir
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BUILD_DIR=build
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rm -rf ${BUILD_DIR}
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mkdir ${BUILD_DIR}
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cd ${BUILD_DIR}
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cmake .. \
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-DPADDLE_LIB=${LIB_DIR} \
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-DWITH_MKL=ON \
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-DDEMO_NAME=ocr_system \
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-DWITH_GPU=OFF \
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-DWITH_STATIC_LIB=OFF \
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-DUSE_TENSORRT=OFF \
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-DOPENCV_DIR=${OPENCV_DIR} \
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-DCUDNN_LIB=${CUDNN_LIB_DIR} \
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-DCUDA_LIB=${CUDA_LIB_DIR} \
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make -j
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```
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`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/`.
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* After the compilation is completed, an executable file named `ocr_system` will be generated in the `build` folder.
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### Run the demo
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* Execute the following command to complete the OCR recognition and detection of an image.
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```shell
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sh tools/run.sh
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```
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The detection results will be shown on the screen, which is as follows.
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<div align="center">
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<img src="../imgs/cpp_infer_pred_12.png" width="600">
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</div>
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### 2.3 Note
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* `MKLDNN` is disabled by default for C++ inference (`use_mkldnn` in `tools/config.txt` is set to 0), if you need to use MKLDNN for inference acceleration, you need to modify `use_mkldnn` to 1, and use the latest version of the Paddle source code to compile the inference library. When using MKLDNN for CPU prediction, if multiple images are predicted at the same time, there will be a memory leak problem (the problem is not present if MKLDNN is disabled). The problem is currently being fixed, and the temporary solution is: when predicting multiple pictures, Re-initialize the recognition (`CRNNRecognizer`) and detection class (`DBDetector`) every 30 pictures or so.
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# Quick start of Chinese OCR model
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## 1. Prepare for the environment
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Please refer to [quick installation](./installation_en.md) to configure the PaddleOCR operating environment.
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## 2.inference models
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| Name | Introduction | Detection model | Recognition model | Recognition model with space support |
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|chinese_db_crnn_mobile| Ultra-lightweight Chinese OCR model |[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance.tar)
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|chinese_db_crnn_server| Universal Chinese OCR model |[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance.tar)
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* If wget is not installed in the windows environment, you can copy the link to the browser to download when downloading the model, and uncompress it and place it in the corresponding directory.
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Copy the download address of the `inference model` for detection and recognition in the table above, and uncompress them.
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```
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mkdir inference && cd inference
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# Download the detection model and unzip
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wget {url/of/detection/inference_model} && tar xf {name/of/detection/inference_model/package}
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# Download the recognition model and unzip
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wget {url/of/recognition/inference_model} && tar xf {name/of/recognition/inference_model/package}
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cd ..
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```
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Take the ultra-lightweight model as an example:
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```
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mkdir inference && cd inference
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# Download the detection model of the ultra-lightweight Chinese OCR model and uncompress it
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wget https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar && tar xf ch_det_mv3_db_infer.tar
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# Download the recognition model of the ultra-lightweight Chinese OCR model and uncompress it
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wget https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar && tar xf ch_rec_mv3_crnn_infer.tar
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cd ..
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```
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After decompression, the file structure should be as follows:
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```
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|-inference
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|-ch_rec_mv3_crnn
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|- model
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|- params
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|-ch_det_mv3_db
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|- model
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|- params
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...
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```
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## 3. Single image or image set prediction
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* The following code implements text detection and recognition process. When performing prediction, you need to specify the path of a single image or image set through the parameter `image_dir`, the parameter `det_model_dir` specifies the path to detect the inference model, and the parameter `rec_model_dir` specifies the path to identify the inference model. The visual results are saved to the `./inference_results` folder by default.
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```bash
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# Predict a single image specified by image_dir
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python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_det_mv3_db/" --rec_model_dir="./inference/ch_rec_mv3_crnn/"
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# Predict imageset specified by image_dir
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python3 tools/infer/predict_system.py --image_dir="./doc/imgs/" --det_model_dir="./inference/ch_det_mv3_db/" --rec_model_dir="./inference/ch_rec_mv3_crnn/"
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# If you want to use the CPU for prediction, you need to set the use_gpu parameter to False
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python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_det_mv3_db/" --rec_model_dir="./inference/ch_rec_mv3_crnn/" --use_gpu=False
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```
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- Universal Chinese OCR model
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Please follow the above steps to download the corresponding models and update the relevant parameters, The example is as follows.
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```
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# Predict a single image specified by image_dir
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python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_det_r50_vd_db/" --rec_model_dir="./inference/ch_rec_r34_vd_crnn/"
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```
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- Universal Chinese OCR model with the support of space
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Please follow the above steps to download the corresponding models and update the relevant parameters, The example is as follows.
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* Note: Please update the source code to the latest version and add parameters `--use_space_char=True`
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```
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# Predict a single image specified by image_dir
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python3 tools/infer/predict_system.py --image_dir="./doc/imgs_en/img_12.jpg" --det_model_dir="./inference/ch_det_r50_vd_db/" --rec_model_dir="./inference/ch_rec_r34_vd_crnn_enhance/" --use_space_char=True
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```
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For more text detection and recognition tandem reasoning, please refer to the document tutorial
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: [Inference with Python inference engine](./inference_en.md)。
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In addition, the tutorial also provides other deployment methods for the Chinese OCR model:
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- [Server-side C++ inference](../../deploy/cpp_infer/readme_en.md)
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- [Service deployment](./serving_en.md)
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- [End-to-end deployment](../../deploy/lite/readme_en.md)
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