@@ -122,8 +122,7 @@ For a new language request, please refer to [Guideline for new language_requests
<imgsrc="./doc/ppocr_framework.png"width="800">
</div>
PP-OCR is a practical ultra-lightweight OCR system. It is mainly composed of three parts: DB text detection, detection frame correction and CRNN text recognition. The system adopts 19 effective strategies from 8 aspects including backbone network selection and adjustment, prediction head design, data augmentation, learning rate transformation strategy, regularization parameter selection, pre-training model use, and automatic model tailoring and quantization to optimize and slim down the models of each module. The final results are an ultra-lightweight Chinese and English OCR model with an overall size of 3.5M and a 2.8M English digital OCR model. For more details, please refer to the PP-OCR technical article (https://arxiv.org/abs/2009.09941). Besides, The implementation of the FPGM Pruner and PACT quantization is based on [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim).
PP-OCR is a practical ultra-lightweight OCR system. It is mainly composed of three parts: DB text detection[2], detection frame correction and CRNN text recognition[7]. The system adopts 19 effective strategies from 8 aspects including backbone network selection and adjustment, prediction head design, data augmentation, learning rate transformation strategy, regularization parameter selection, pre-training model use, and automatic model tailoring and quantization to optimize and slim down the models of each module. The final results are an ultra-lightweight Chinese and English OCR model with an overall size of 3.5M and a 2.8M English digital OCR model. For more details, please refer to the PP-OCR technical article (https://arxiv.org/abs/2009.09941). Besides, The implementation of the FPGM Pruner [8] and PACT quantization [9] is based on [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim).
The Style-Text data synthesis tool is a tool based on Baidu's self-developed text editing algorithm "Editing Text in the Wild" [https://arxiv.org/abs/1908.03047](https://arxiv.org/abs/1908.03047).
The Style-Text data synthesis tool is a tool based on Baidu and HUST cooperation research work, "Editing Text in the Wild" [https://arxiv.org/abs/1908.03047](https://arxiv.org/abs/1908.03047).
Different from the commonly used GAN-based data synthesis tools, the main framework of Style-Text includes:
* (1) Text foreground style transfer module.
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@@ -69,12 +69,14 @@ fusion_generator:
1. You can run `tools/synth_image` and generate the demo image, which is saved in the current folder.
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/`.
* After the compilation process, you can see the following files in the folder of `build/paddle_inference_install_dir/`.
```
build/fluid_inference_install_dir/
build/paddle_inference_install_dir/
|-- CMakeCache.txt
|-- paddle
|-- third_party
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@@ -130,10 +130,10 @@ Among them, `paddle` is the Paddle library required for C++ prediction later, an
* After downloading, use the following method to uncompress.
```
tar -xf fluid_inference.tgz
tar -xf paddle_inference.tgz
```
Finally you can see the following files in the folder of `fluid_inference/`.
Finally you can see the following files in the folder of `paddle_inference/`.
## 2. Compile and run the demo
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@@ -145,11 +145,11 @@ Finally you can see the following files in the folder of `fluid_inference/`.
```
inference/
|-- det_db
| |--model
| |--params
| |--inference.pdparams
| |--inference.pdimodel
|-- rec_rcnn
| |--model
| |--params
| |--inference.pdparams
| |--inference.pdparams
```
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@@ -188,7 +188,9 @@ cmake .. \
make -j
```
`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/`.
`OPENCV_DIR` is the opencv installation path; `LIB_DIR` is the download (`paddle_inference` folder)
or the generated Paddle inference library path (`build/paddle_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.
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@@ -211,7 +213,6 @@ gpu_id 0 # GPU id when use_gpu is 1
gpu_mem 4000 # GPU memory requested
cpu_math_library_num_threads 10 # Number of threads when using CPU inference. When machine cores is enough, the large the value, the faster the inference speed
use_mkldnn 1 # Whether to use mkdlnn library
use_zero_copy_run 1 # Whether to use use_zero_copy_run for inference
max_side_len 960 # Limit the maximum image height and width to 960
det_db_thresh 0.3 # Used to filter the binarized image of DB prediction, setting 0.-0.3 has no obvious effect on the result
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@@ -244,4 +245,4 @@ The detection results will be shown on the screen, which is as follows.
### 2.3 Notes
* Paddle2.0.0-beta0 inference model library is recommanded for this tuturial.
* Paddle2.0.0-beta0 inference model library is recommended for this toturial.
