152 lines
5.1 KiB
Markdown
152 lines
5.1 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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- 超轻量级模型
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- (检测模型4.1M + 识别模型4.5M = 8.6M)
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- 支持竖排文字识别
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- (单模型同时支持横排和竖排文字识别)
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- 支持长文本识别
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- 支持中英文数字组合识别
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- 提供训练代码
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- 支持模型部署
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## 文档教程
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- [快速安装](./doc/installation.md)
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- [文本识别模型训练/评估/预测](./doc/detection.md)
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- [文本预测模型训练/评估/预测](./doc/recognition.md)
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- [基于inference model预测](./doc/)
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### **快速开始**
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下载inference模型
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```
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# 创建inference模型保存目录
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mkdir inference && cd inference && mkdir det && mkdir rec
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# 下载检测inference模型
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wget -P ./inference/det 检测inference模型链接
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# 下载识别inference模型
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wget -P ./inferencee/rec 识别inference模型链接
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```
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实现文本检测、识别串联推理，预测$image_dir$指定的单张图像：
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```
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export PYTHONPATH=.
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python tools/infer/predict_eval.py --image_dir="/Demo.jpg" --det_model_dir="./inference/det/" --rec_model_dir="./inference/rec/"
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```
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在执行预测时，通过参数det_model_dir以及rec_model_dir设置存储inference 模型的路径。
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实现文本检测、识别串联推理，预测$image_dir$指指定文件夹下的所有图像：
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```
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python tools/infer/predict_eval.py --image_dir="/test_imgs/" --det_model_dir="./inference/det/" --rec_model_dir="./inference/rec/"
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```
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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](https://arxiv.org/abs/1704.03155)
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- [x] [DB](https://arxiv.org/abs/1911.08947)
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- [ ] [SAST](https://arxiv.org/abs/1908.05498)
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算法效果：
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|模型|骨干网络|Hmean|
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|EAST|ResNet50_vd|85.85%|
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|EAST|MobileNetV3|79.08%|
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|DB|ResNet50_vd|83.30%|
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|DB|MobileNetV3|73.00%|
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PaddleOCR文本检测算法的训练与使用请参考[文档](./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](https://arxiv.org/abs/1507.05717)
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- [x] [DTRB](https://arxiv.org/abs/1904.01906)
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- [ ] [Rosetta](https://arxiv.org/abs/1910.05085)
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- [ ] [STAR-Net](http://www.bmva.org/bmvc/2016/papers/paper043/index.html)
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- [ ] [RARE](https://arxiv.org/abs/1603.03915v1)
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- [ ] [SRN]((https://arxiv.org/abs/2003.12294))(百度自研)
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算法效果如下表所示，精度指标是在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上的评测结果的平均值。
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|模型|骨干网络|ACC|
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|Rosetta|Resnet34_vd|80.24%|
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|Rosetta|MobileNetV3|78.16%|
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|CRNN|Resnet34_vd|82.20%|
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|CRNN|MobileNetV3|79.37%|
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|STAR-Net|Resnet34_vd|83.93%|
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|STAR-Net|MobileNetV3|81.56%|
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|RARE|Resnet34_vd|84.90%|
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|RARE|MobileNetV3|83.32%|
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PaddleOCR文本识别算法的训练与使用请参考[文档](./doc/recognition.md)。
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## TODO
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**端到端OCR算法**
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PaddleOCR即将开源百度自研端对端OCR模型[End2End-PSL](https://arxiv.org/abs/1909.07808)，敬请关注。
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- [ ] End2End-PSL (comming soon)
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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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