178 lines
8.0 KiB
Markdown
178 lines
8.0 KiB
Markdown
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## 简介
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PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力使用者训练出更好的模型，并应用落地。
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## 特性
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- 超轻量级中文OCR，总模型仅8.6M
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- 单模型支持中英文数字组合识别、竖排文本识别、长文本识别
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- 检测模型DB（4.1M）+识别模型CRNN（4.5M）
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- 多种文本检测训练算法，EAST、DB
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- 多种文本识别训练算法，Rosetta、CRNN、STAR-Net、RARE
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## **超轻量级中文OCR体验**
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上图是超轻量级中文OCR模型效果展示，更多效果图请见文末[效果展示](#效果展示)。
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#### 1.环境配置
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请先参考[快速安装](./doc/installation.md)配置PaddleOCR运行环境。
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#### 2.模型下载
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```
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# 下载inference模型文件包
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wget https://paddleocr.bj.bcebos.com/inference.tar
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# inference模型文件包解压
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tar -xf inference.tar
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```
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#### 3.单张图像或者图像集合预测
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以下代码实现了文本检测、识别串联推理，在执行预测时，需要通过参数image_dir指定单张图像或者图像集合的路径、参数det_model_dir指定检测inference模型的路径和参数rec_model_dir指定识别inference模型的路径。可视化识别结果默认保存到 ./inference_results 文件夹里面。
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```
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# 设置PYTHONPATH环境变量
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export PYTHONPATH=.
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# 预测image_dir指定的单张图像
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python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/det/" --rec_model_dir="./inference/rec/"
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# 预测image_dir指定的图像集合
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python3 tools/infer/predict_system.py --image_dir="./doc/imgs/" --det_model_dir="./inference/det/" --rec_model_dir="./inference/rec/"
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# 如果想使用CPU进行预测，执行命令如下
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python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/det/" --rec_model_dir="./inference/rec/" --use_gpu=False
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```
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更多的文本检测、识别串联推理使用方式请参考文档教程中[基于预测引擎推理](./doc/inference.md)。
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## 文档教程
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- [快速安装](./doc/installation.md)
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- [文本检测模型训练/评估/预测](./doc/detection.md)（持续更新中）
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- [文本识别模型训练/评估/预测](./doc/recognition.md)（持续更新中）
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- [基于预测引擎推理](./doc/inference.md)（持续更新中）
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## 文本检测算法
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PaddleOCR开源的文本检测算法列表：
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- [x] EAST([paper](https://arxiv.org/abs/1704.03155))
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- [x] DB([paper](https://arxiv.org/abs/1911.08947))
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- [ ] SAST([paper](https://arxiv.org/abs/1908.05498))(百度自研, comming soon)
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在ICDAR2015文本检测公开数据集上，算法效果如下：
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|模型|骨干网络|precision|recall|Hmean|下载链接|
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|EAST|ResNet50_vd|88.18%|85.51|86.82%|[下载链接](https://paddleocr.bj.bcebos.com/det_r50_vd_east.tar)|
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|EAST|MobileNetV3|81.67%|79.83%|80.74%|[下载链接](https://paddleocr.bj.bcebos.com/det_mv3_east.tar)|
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|DB|ResNet50_vd|83.79%|80.65%|82.19%|[下载链接](https://paddleocr.bj.bcebos.com/det_r50_vd_db.tar)|
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|DB|MobileNetV3|75.92%|73.18%|74.53%|[下载链接](https://paddleocr.bj.bcebos.com/det_mv3_db.tar)|
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* 注： 上述DB模型的训练和评估，需设置后处理参数box_thresh=0.6，unclip_ratio=1.5，使用不同数据集、不同模型训练，可调整这两个参数进行优化
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PaddleOCR文本检测算法的训练和使用请参考文档教程中[文本检测模型训练/评估/预测](./doc/detection.md)。
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## 文本识别算法
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PaddleOCR开源的文本识别算法列表：
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- [x] CRNN([paper](https://arxiv.org/abs/1507.05717))
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- [x] Rosetta([paper](https://arxiv.org/abs/1910.05085))
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- [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))
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- [x] RARE([paper](https://arxiv.org/abs/1603.03915v1))
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- [ ] SRN([paper](https://arxiv.org/abs/2003.12294))(百度自研, comming soon)
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参考[DTRB](https://arxiv.org/abs/1904.01906)文字识别训练和评估流程，使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法效果如下：
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|模型|骨干网络|Avg Accuracy|模型存储命名|下载链接|
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|Rosetta|Resnet34_vd|80.24%|rec_r34_vd_none_none_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_none_ctc.tar)|
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|Rosetta|MobileNetV3|78.16%|rec_mv3_none_none_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_none_none_ctc.tar)|
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|CRNN|Resnet34_vd|82.20%|rec_r34_vd_none_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_bilstm_ctc.tar)|
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|CRNN|MobileNetV3|79.37%|rec_mv3_none_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_none_bilstm_ctc.tar)|
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|STAR-Net|Resnet34_vd|83.93%|rec_r34_vd_tps_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_ctc.tar)|
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|STAR-Net|MobileNetV3|81.56%|rec_mv3_tps_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_ctc.tar)|
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|RARE|Resnet34_vd|84.90%|rec_r34_vd_tps_bilstm_attn|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_attn.tar)|
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|RARE|MobileNetV3|83.32%|rec_mv3_tps_bilstm_attn|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_attn.tar)|
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PaddleOCR文本识别算法的训练和使用请参考文档教程中[文本识别模型训练/评估/预测](./doc/recognition.md)。
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## 端到端OCR算法
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- [ ] [End2End-PSL](https://arxiv.org/abs/1909.07808)(百度自研, comming soon)
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<a name="效果展示"></a>
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## 效果展示
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## 参考文献
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```
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1. EAST:
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@inproceedings{zhou2017east,
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title={EAST: an efficient and accurate scene text detector},
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author={Zhou, Xinyu and Yao, Cong and Wen, He and Wang, Yuzhi and Zhou, Shuchang and He, Weiran and Liang, Jiajun},
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booktitle={Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},
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pages={5551--5560},
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year={2017}
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}
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2. DB:
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@article{liao2019real,
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title={Real-time Scene Text Detection with Differentiable Binarization},
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author={Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang},
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journal={arXiv preprint arXiv:1911.08947},
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year={2019}
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}
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3. DTRB:
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@inproceedings{baek2019wrong,
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title={What is wrong with scene text recognition model comparisons? dataset and model analysis},
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author={Baek, Jeonghun and Kim, Geewook and Lee, Junyeop and Park, Sungrae and Han, Dongyoon and Yun, Sangdoo and Oh, Seong Joon and Lee, Hwalsuk},
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booktitle={Proceedings of the IEEE International Conference on Computer Vision},
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pages={4715--4723},
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year={2019}
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}
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4. SAST:
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@inproceedings{wang2019single,
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title={A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning},
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author={Wang, Pengfei and Zhang, Chengquan and Qi, Fei and Huang, Zuming and En, Mengyi and Han, Junyu and Liu, Jingtuo and Ding, Errui and Shi, Guangming},
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booktitle={Proceedings of the 27th ACM International Conference on Multimedia},
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pages={1277--1285},
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year={2019}
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}
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5. SRN:
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@article{yu2020towards,
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title={Towards Accurate Scene Text Recognition with Semantic Reasoning Networks},
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author={Yu, Deli and Li, Xuan and Zhang, Chengquan and Han, Junyu and Liu, Jingtuo and Ding, Errui},
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journal={arXiv preprint arXiv:2003.12294},
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year={2020}
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}
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6. end2end-psl:
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@inproceedings{sun2019chinese,
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title={Chinese Street View Text: Large-scale Chinese Text Reading with Partially Supervised Learning},
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author={Sun, Yipeng and Liu, Jiaming and Liu, Wei and Han, Junyu and Ding, Errui and Liu, Jingtuo},
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booktitle={Proceedings of the IEEE International Conference on Computer Vision},
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pages={9086--9095},
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year={2019}
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}
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```
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## 许可证书
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本项目的发布受<a href="https://github.com/PaddlePaddle/PaddleOCR/blob/master/LICENSE">Apache 2.0 license</a>许可认证。
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## 版本更新
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## 如何贡献代码
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我们非常欢迎你为PaddleOCR贡献代码，也十分感谢你的反馈。
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