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paddleocr's Introduction

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

  • 超轻量级中文OCR,总模型仅8.6M
    • 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
    • 检测模型DB(4.1M)+识别模型CRNN(4.5M)
  • 多种文本检测训练算法,EAST、DB
  • 多种文本识别训练算法,Rosetta、CRNN、STAR-Net、RARE

超轻量级中文OCR体验

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

1.环境配置

请先参考快速安装配置PaddleOCR运行环境。

2.模型下载

# 下载inference模型文件包
wget https://paddleocr.bj.bcebos.com/inference.tar
# inference模型文件包解压
tar -xf inference.tar

3.单张图像或者图像集合预测

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。

# 设置PYTHONPATH环境变量
export PYTHONPATH=.

# 预测image_dir指定的单张图像
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/det/"  --rec_model_dir="./inference/rec/"

# 预测image_dir指定的图像集合
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/" --det_model_dir="./inference/det/"  --rec_model_dir="./inference/rec/"

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

文档教程

文本检测算法

PaddleOCR开源的文本检测算法列表:

在ICDAR2015文本检测公开数据集上,算法效果如下:

模型 骨干网络 Hmean 下载链接
EAST ResNet50_vd 85.85% 下载链接
EAST MobileNetV3 79.08% 下载链接
DB ResNet50_vd 83.30% 下载链接
DB MobileNetV3 73.00% 下载链接

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

文本识别算法

PaddleOCR开源的文本识别算法列表:

参考DTRB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:

模型 骨干网络 Avg Accuracy 下载链接
Rosetta Resnet34_vd 80.24% 下载链接
Rosetta MobileNetV3 78.16% 下载链接
CRNN Resnet34_vd 82.20% 下载链接
CRNN MobileNetV3 79.37% 下载链接
STAR-Net Resnet34_vd 83.93% 下载链接
STAR-Net MobileNetV3 81.56% 下载链接
RARE Resnet34_vd 84.90% 下载链接
RARE MobileNetV3 83.32% 下载链接

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

端到端OCR算法

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

如何贡献代码

我们非常欢迎你为PaddleOCR贡献代码,也十分感谢你的反馈。

paddleocr's People

Contributors

dyning avatar tink2123 avatar ldoublev avatar xiaoguanghu01 avatar

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