# Tutorial of PaddleOCR Mobile deployment This tutorial will introduce how to use [paddle-lite](https://github.com/PaddlePaddle/Paddle-Lite) to deploy paddleOCR ultra-lightweight Chinese and English detection models on mobile phones. paddle-lite is a lightweight inference engine for PaddlePaddle. It provides efficient inference capabilities for mobile phones and IoTs, and extensively integrates cross-platform hardware to provide lightweight deployment solutions for end-side deployment issues. ## 1. Preparation - Computer (for Compiling Paddle Lite) - Mobile phone (arm7 or arm8) ## 2. Build PaddleLite library 1. [Docker](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#docker) 2. [Linux](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#linux) 3. [MAC OS](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#mac-os) ## 3. Download prebuild library for android and ios |Platform|Prebuild library Download Link| |-|-| |Android|[arm7](https://paddlelite-data.bj.bcebos.com/Release/2.6.1/Android/inference_lite_lib.android.armv7.gcc.c++_static.with_extra.CV_ON.tar.gz) / [arm8](https://paddlelite-data.bj.bcebos.com/Release/2.6.1/Android/inference_lite_lib.android.armv8.gcc.c++_static.with_extra.CV_ON.tar.gz)| |IOS|[arm7](https://paddlelite-data.bj.bcebos.com/Release/2.6.1/iOS/inference_lite_lib.ios.armv7.with_extra.CV_ON.tar.gz) / [arm8](https://paddlelite-data.bj.bcebos.com/Release/2.6.1/iOS/inference_lite_lib.ios64.armv8.with_extra.CV_ON.tar.gz)| note: It is recommended to build prebuild library using [Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite) develop branch if developer wants to deploy the [quantitative](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/slim/quantization/README_en.md) model to mobile phone. The structure of the prediction library is as follows: ``` inference_lite_lib.android.armv8/ |-- cxx C++ prebuild library | |-- include C++ | | |-- paddle_api.h | | |-- paddle_image_preprocess.h | | |-- paddle_lite_factory_helper.h | | |-- paddle_place.h | | |-- paddle_use_kernels.h | | |-- paddle_use_ops.h | | `-- paddle_use_passes.h | `-- lib | |-- libpaddle_api_light_bundled.a C++ static library | `-- libpaddle_light_api_shared.so C++ dynamic library |-- java Java predict library | |-- jar | | `-- PaddlePredictor.jar | |-- so | | `-- libpaddle_lite_jni.so | `-- src |-- demo C++ and java demo | |-- cxx | `-- java ``` ## 4. Inference Model Optimization Paddle Lite provides a variety of strategies to automatically optimize the original training model, including quantization, sub-graph fusion, hybrid scheduling, Kernel optimization and so on. In order to make the optimization process more convenient and easy to use, Paddle Lite provide opt tools to automatically complete the optimization steps and output a lightweight, optimal executable model. If you use PaddleOCR 8.6M OCR model to deploy, you can directly download the optimized model. |Version|Introduction|Model size|Detection model|Text Direction model|Recognition model|Paddle Lite branch | |-|-|-|-|-|-| |V1.1|extra-lightweight chinese OCR optimized model|3.5M|[Download](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/det/ch_ppocr_mobile_v1.1_det_prune_opt.nb)|[Download](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_cls_quant_opt.nb)|[Download](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/rec/ch_ppocr_mobile_v1.1_rec_quant_opt.nb)|develop| |V1.0|lightweight Chinese OCR optimized model|8.6M|[Download](https://paddleocr.bj.bcebos.com/deploy/lite/ch_det_mv3_db_opt.nb)|---|[Download](https://paddleocr.bj.bcebos.com/deploy/lite/ch_rec_mv3_crnn_opt.nb)|develop| If the model to be deployed is not in the above table, you need to follow the steps below to obtain the optimized model. ``` git clone https://github.com/PaddlePaddle/Paddle-Lite.git cd Paddle-Lite git checkout develop ./lite/tools/build.sh build_optimize_tool ``` The `opt` tool can be obtained by compiling Paddle Lite. After the compilation is complete, the opt file is located under `build.opt/lite/api/`. The `opt` can optimize the inference model saved by paddle.io.save_inference_model to get the model that the paddlelite API can use. The usage of opt is as follows： ``` # 【Recommend】V1.1 is better than V1.0. steps for convert V1.1 model to nb file are as follows wget https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/det/ch_ppocr_mobile_v1.1_det_prune_infer.tar && tar xf ch_ppocr_mobile_v1.1_det_prune_infer.tar wget https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/rec/ch_ppocr_mobile_v1.1_rec_quant_infer.tar && tar xf ch_ppocr_mobile_v1.1_rec_quant_infer.tar ./opt --model_file=./ch_ppocr_mobile_v1.1_det_prune_infer/model --param_file=./ch_ppocr_mobile_v1.1_det_prune_infer/params --optimize_out=./ch_ppocr_mobile_v1.1_det_prune_opt --valid_targets=arm ./opt --model_file=./ch_ppocr_mobile_v1.1_rec_quant_infer/model --param_file=./ch_ppocr_mobile_v1.1_rec_quant_infer/params --optimize_out=./ch_ppocr_mobile_v1.1_rec_quant_opt --valid_targets=arm # or use V1.0 model wget https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar && tar xf ch_det_mv3_db_infer.tar wget https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar && tar xf ch_rec_mv3_crnn_infer.tar ./opt --model_file=./ch_det_mv3_db/model --param_file=./ch_det_mv3_db/params --optimize_out_type=naive_buffer --optimize_out=./ch_det_mv3_db_opt --valid_targets=arm ./opt --model_file=./ch_rec_mv3_crnn/model --param_file=./ch_rec_mv3_crnn/params --optimize_out_type=naive_buffer --optimize_out=./ch_rec_mv3_crnn_opt --valid_targets=arm ``` When the above code command is completed, there will be two more files `.nb` in the current directory, which is the converted model file. ## 5. Run optimized model on Phone 1. Prepare an Android phone with arm8. If the compiled prediction library and opt file are armv7, you need an arm7 phone and modify ARM_ABI = arm7 in the Makefile. 2. Make sure the phone is connected to the computer, open the USB debugging option of the phone, and select the file transfer mode. 3. Install the adb tool on the computer. 3.1 Install ADB for MAC ``` brew cask install android-platform-tools ``` 3.2 Install ADB for Linux ``` sudo apt update sudo apt install -y wget adb ``` 3.3 Install ADB for windows [Download Link](https://developer.android.com/studio) Verify whether adb is installed successfully ``` $ adb devices List of devices attached 744be294 device ``` If there is `device` output, it means the installation was successful. 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 /{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. 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. The structure of the OCR demo is as follows after the above command is executed: ``` demo/cxx/ocr/ |-- debug/ | |--ch_ppocr_mobile_v1.1_det_prune_opt.nb Detection model | |--ch_ppocr_mobile_v1.1_rec_quant_opt.nb Recognition model | |--ch_ppocr_mobile_cls_quant_opt.nb Text direction classification 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 ``` cd inference_lite_lib.android.armv8/demo/cxx/ocr/ make -j mv ocr_db_crnn ./debug/ adb push debug /data/local/tmp/ adb shell cd /data/local/tmp/debug export LD_LIBRARY_PATH=/data/local/tmp/debug:$LD_LIBRARY_PATH # run model ./ocr_db_crnn ch_ppocr_mobile_v1.1_det_prune_opt.nb ch_ppocr_mobile_v1.1_rec_quant_opt.nb ch_ppocr_mobile_cls_quant_opt.nb ./11.jpg ppocr_keys_v1.txt ``` The outputs are as follows: