PaddleOCR/doc

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

文档教程

• 快速安装
• 文本识别模型训练/评估/预测
• 文本预测模型训练/评估/预测
• 基于inference model预测

快速开始

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
wget -P ./inference/det 检测inference模型链接
# 下载识别inference模型
wget -P ./inferencee/rec 识别inference模型链接

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/"

文本检测算法:

PaddleOCR开源的文本检测算法列表：

• EAST
• DB
• SAST

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：

• CRNN
• DTRB
• Rosetta
• STAR-Net
• RARE
• SRN(百度自研)

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 (comming soon)

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}
}