6.4 KiB
6.4 KiB
简介
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。
特性：
- 超轻量级模型
- (检测模型4.1M + 识别模型4.5M = 8.6M)
- 支持竖排文字识别
- (单模型同时支持横排和竖排文字识别)
- 支持长文本识别
- 支持中英文数字组合识别
- 提供训练代码
- 支持模型部署
注：更多效果展示请见文末。
快速运行
运行前请先参考快速安装配置PaddleOCR运行环境。
下载inference模型
# 创建inference模型保存目录
mkdir inference && cd inference && mkdir det && mkdir rec
# 下载检测inference模型/ 识别 inference 模型
wget -P ./inference https://paddleocr.bj.bcebos.com/inference.tar
实现文本检测、识别串联推理，预测image_dir指定的单张图像：
export PYTHONPATH=.
python tools/infer/predict_eval.py --image_dir="/Demo.jpg" --det_model_dir="./inference/det/" --rec_model_dir="./inference/rec/"
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。
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：
python tools/infer/predict_eval.py --image_dir="/test_imgs/" --det_model_dir="./inference/det/" --rec_model_dir="./inference/rec/"
文档教程
文本检测算法:
PaddleOCR开源的文本检测算法列表：
算法效果：
| 模型 | 骨干网络 | Hmean |
|---|---|---|
| EAST | ResNet50_vd | 85.85% |
| EAST | MobileNetV3 | 79.08% |
| DB | ResNet50_vd | 83.30% |
| DB | MobileNetV3 | 73.00% |
PaddleOCR文本检测算法的训练与使用请参考文档。
文本识别算法:
PaddleOCR开源的文本识别算法列表：
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。
| 模型 | 骨干网络 | ACC |
|---|---|---|
| Rosetta | Resnet34_vd | 80.24% |
| Rosetta | MobileNetV3 | 78.16% |
| CRNN | Resnet34_vd | 82.20% |
| CRNN | MobileNetV3 | 79.37% |
| STAR-Net | Resnet34_vd | 83.93% |
| STAR-Net | MobileNetV3 | 81.56% |
| RARE | Resnet34_vd | 84.90% |
| RARE | MobileNetV3 | 83.32% |
PaddleOCR文本识别算法的训练与使用请参考文档。
TODO
端到端OCR算法 PaddleOCR即将开源百度自研端对端OCR模型End2End-PSL，敬请关注。
- End2End-PSL (百度自研, comming soon)
效果展示
参考文献
1. EAST:
@inproceedings{zhou2017east,
title={EAST: an efficient and accurate scene text detector},
author={Zhou, Xinyu and Yao, Cong and Wen, He and Wang, Yuzhi and Zhou, Shuchang and He, Weiran and Liang, Jiajun},
booktitle={Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},
pages={5551--5560},
year={2017}
}
2. DB:
@article{liao2019real,
title={Real-time Scene Text Detection with Differentiable Binarization},
author={Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang},
journal={arXiv preprint arXiv:1911.08947},
year={2019}
}
3. DTRB:
@inproceedings{baek2019wrong,
title={What is wrong with scene text recognition model comparisons? dataset and model analysis},
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},
booktitle={Proceedings of the IEEE International Conference on Computer Vision},
pages={4715--4723},
year={2019}
}
4. SAST:
@inproceedings{wang2019single,
title={A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning},
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},
booktitle={Proceedings of the 27th ACM International Conference on Multimedia},
pages={1277--1285},
year={2019}
}
5. SRN:
@article{yu2020towards,
title={Towards Accurate Scene Text Recognition with Semantic Reasoning Networks},
author={Yu, Deli and Li, Xuan and Zhang, Chengquan and Han, Junyu and Liu, Jingtuo and Ding, Errui},
journal={arXiv preprint arXiv:2003.12294},
year={2020}
}
6. end2end-psl:
@inproceedings{sun2019chinese,
title={Chinese Street View Text: Large-scale Chinese Text Reading with Partially Supervised Learning},
author={Sun, Yipeng and Liu, Jiaming and Liu, Wei and Han, Junyu and Ding, Errui and Liu, Jingtuo},
booktitle={Proceedings of the IEEE International Conference on Computer Vision},
pages={9086--9095},
year={2019}
}
许可证书
本项目的发布受Apache 2.0 license许可认证。
版本更新
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