dyning
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README.md
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README.md
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# PaddleOCR
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OCR algorithms with PaddlePaddle （still under develop)
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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/ 识别 inference 模型
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wget -P ./inference https://paddleocr.bj.bcebos.com/inference.tar
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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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|-|-|-|
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|EAST|[ResNet50_vd](https://paddleocr.bj.bcebos.com/det_r50_vd_east.tar)|85.85%|
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|EAST|[MobileNetV3](https://paddleocr.bj.bcebos.com/det_mv3_east.tar)|79.08%|
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|DB|[ResNet50_vd](https://paddleocr.bj.bcebos.com/det_r50_vd_db.tar)|83.30%|
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|DB|[MobileNetV3](https://paddleocr.bj.bcebos.com/det_mv3_db.tar)|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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|-|-|-|
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|Rosetta|[Resnet34_vd](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_none_ctc.tar)|80.24%|
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|Rosetta|[MobileNetV3](https://paddleocr.bj.bcebos.com/rec_mv3_none_none_ctc.tar)|78.16%|
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|CRNN|[Resnet34_vd](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_bilstm_ctc.tar)|82.20%|
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|CRNN|[MobileNetV3](https://paddleocr.bj.bcebos.com/rec_mv3_none_bilstm_ctc.tar)|79.37%|
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|STAR-Net|[Resnet34_vd](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_ctc.tar)|83.93%|
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|STAR-Net|[MobileNetV3](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_ctc.tar)|81.56%|
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|RARE|[Resnet34_vd](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_attn.tar)|84.90%|
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|RARE|[MobileNetV3](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_attn.tar)|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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@ -0,0 +1,42 @@
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Global:
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algorithm: CRNN
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use_gpu: true
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epoch_num: 3000
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log_smooth_window: 20
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print_batch_step: 10
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save_model_dir: ./output/rec_CRNN
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save_epoch_step: 3
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eval_batch_step: 2000
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train_batch_size_per_card: 256
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test_batch_size_per_card: 256
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image_shape: [3, 32, 100]
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max_text_length: 25
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character_type: ch
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character_dict_path: ./ppocr/utils/ppocr_keys_v1.txt
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loss_type: ctc
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reader_yml: ./configs/rec/rec_chinese_reader.yml
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pretrain_weights: ./pretrain_models/CRNN/best_accuracy
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checkpoints:
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save_inference_dir:
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Architecture:
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function: ppocr.modeling.architectures.rec_model,RecModel
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Backbone:
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function: ppocr.modeling.backbones.rec_mobilenet_v3,MobileNetV3
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scale: 0.5
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model_name: small
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Head:
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function: ppocr.modeling.heads.rec_ctc_head,CTCPredict
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encoder_type: rnn
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SeqRNN:
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hidden_size: 48
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Loss:
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function: ppocr.modeling.losses.rec_ctc_loss,CTCLoss
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Optimizer:
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function: ppocr.optimizer,AdamDecay
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base_lr: 0.0005
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beta1: 0.9
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beta2: 0.999
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TrainReader:
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reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
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num_workers: 8
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img_set_dir: ./train_data
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label_file_path: ./train_data/rec_gt_train.txt
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EvalReader:
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reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
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img_set_dir: ./train_data
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label_file_path: ./train_data/rec_gt_test.txt
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TestReader:
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reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
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infer_img: ./infer_img
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@ -11,8 +11,8 @@ Global:
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test_batch_size_per_card: 256
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image_shape: [3, 32, 100]
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max_text_length: 25
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character_type: ch
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character_dict_path: ./ppocr/utils/ic15_dict.txt
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character_type: en
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character_dict_path: /workspace/PaddleOCR/train_data/ic15_dict.txt
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loss_type: ctc
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reader_yml: ./configs/rec/rec_icdar15_reader.yml
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pretrain_weights: ./pretrain_models/CRNN/best_accuracy
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@ -24,13 +24,13 @@ Architecture:
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Backbone:
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function: ppocr.modeling.backbones.rec_mobilenet_v3,MobileNetV3
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scale: 0.5
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model_name: small
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model_name: large
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Head:
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function: ppocr.modeling.heads.rec_ctc_head,CTCPredict
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encoder_type: rnn
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SeqRNN:
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hidden_size: 48
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hidden_size: 96
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Loss:
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function: ppocr.modeling.losses.rec_ctc_loss,CTCLoss
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151
doc/README.md
151
doc/README.md
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@ -1,151 +0,0 @@
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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。
|
||||
|
||||
## 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
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||||
- [快速安装](./doc/installation.md)
|
||||
- [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)
|
||||
- [基于inference model预测](./doc/)
|
||||
|
||||
### **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
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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
|
||||
```
|
||||
|
||||
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/"
|
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```
|
||||
|
||||
|
||||
|
||||
## 文本检测算法:
|
||||
|
||||
PaddleOCR开源的文本检测算法列表：
|
||||
- [x] [EAST](https://arxiv.org/abs/1704.03155)
|
||||
- [x] [DB](https://arxiv.org/abs/1911.08947)
|
||||
- [ ] [SAST](https://arxiv.org/abs/1908.05498)
|
||||
|
||||
|
||||
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](./doc/detection.md)。
|
||||
|
||||
## 文本识别算法:
|
||||
|
||||
PaddleOCR开源的文本识别算法列表：
|
||||
- [x] [CRNN](https://arxiv.org/abs/1507.05717)
|
||||
- [x] [DTRB](https://arxiv.org/abs/1904.01906)
|
||||
- [ ] [Rosetta](https://arxiv.org/abs/1910.05085)
|
||||
- [ ] [STAR-Net](http://www.bmva.org/bmvc/2016/papers/paper043/index.html)
|
||||
- [ ] [RARE](https://arxiv.org/abs/1603.03915v1)
|
||||
- [ ] [SRN]((https://arxiv.org/abs/2003.12294))(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](./doc/recognition.md)。
|
||||
|
||||
## TODO
|
||||
**端到端OCR算法**
|
||||
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。
|
||||
- [ ] 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}
|
||||
}
|
||||
```
|
||||
|
|
@ -0,0 +1,35 @@
|
|||
# 可选参数列表
|
||||
|
||||
以下列表可以通过`--help`查看
|
||||
|
||||
| FLAG | 支持脚本 | 用途 | 默认值 | 备注 |
|
||||
| :----------------------: | :------------: | :---------------: | :--------------: | :-----------------: |
|
||||
| -c | ALL | 指定配置文件 | None | **配置模块说明请参考 参数介绍** |
|
||||
| -o | ALL | 设置配置文件里的参数内容 | None | 使用-o配置相较于-c选择的配置文件具有更高的优先级。例如：`-o Global.use_gpu=false` |
|
||||
|
||||
|
||||
## 配置文件 Global 参数介绍
|
||||
|
||||
| 字段 | 用途 | 默认值 | 备注 |
|
||||
| :----------------------: | :---------------------: | :--------------: | :--------------------: |
|
||||
| algorithm | 设置算法 | CRNN | 选择模型，支持模型请参考[简介]() |
|
||||
| use_gpu | 设置代码运行场所 | true | \ |
|
||||
| epoch_num | 最大训练epoch数 | 3000 | \ |
|
||||
| log_smooth_window | 滑动窗口大小 | 20 | \ |
|
||||
| print_batch_step | 设置打印log间隔 | 10 | \ |
|
||||
| save_model_dir | 设置模型保存路径 | output/rec_CRNN | \ |
|
||||
| save_epoch_step | 设置模型保存间隔 | 3 | \ |
|
||||
| eval_batch_step | 设置模型评估间隔 | 2000 | \ |
|
||||
|train_batch_size_per_card | 设置训练时单卡batch size | 256 | \ |
|
||||
| test_batch_size_per_card | 设置评估时单卡batch size | 256 | \ |
|
||||
| image_shape | 设置输入图片尺寸 | [3, 32, 100] | \ |
|
||||
| max_text_length | 设置文本最大长度 | 25 | \ |
|
||||
| character_type | 设置字符类型 | ch | en/ch, en时将使用默认dict，ch时使用自定义dict|
|
||||
| character_dict_path | 设置字典路径 | ./ppocr/utils/ic15_dict.txt | \ |
|
||||
| loss_type | 设置 loss 类型 | ctc | 支持两种loss： ctc / attention |
|
||||
| reader_yml | 设置reader配置文件 | ./configs/rec/rec_icdar15_reader.yml | \ |
|
||||
| pretrain_weights | 加载预训练模型路径 | ./pretrain_models/CRNN/best_accuracy | \ |
|
||||
| checkpoints | 加载模型参数路径 | None | 用于中断后重新训练 |
|
||||
| save_inference_dir | inference model 保存路径 | None | 用于保存inference model |
|
||||
|
||||
|
||||
|
|
@ -8,8 +8,8 @@ icdar2015数据集可以从[官网](https://rrc.cvc.uab.es/?ch=4&com=downloads)
|
|||
将下载到的数据集解压到工作目录下，假设解压在/PaddleOCR/train_data/ 下。另外，PaddleOCR将零散的标注文件整理成单独的标注文件
|
||||
，您可以通过wget的方式进行下载。
|
||||
```
|
||||
wget -P /PaddleOCR/train_data/ 训练标注文件链接
|
||||
wget -P /PaddleOCR/train_data/ 测试标注文件链接
|
||||
wget -P /PaddleOCR/train_data/ https://paddleocr.bj.bcebos.com/dataset%2Ftrain_icdar2015_label.txt
|
||||
wget -P /PaddleOCR/train_data/ https://paddleocr.bj.bcebos.com/dataset%2Ftest_icdar2015_label.txt
|
||||
```
|
||||
|
||||
解压数据集和下载标注文件后，/PaddleOCR/train_data/ 有两个文件夹和两个文件，分别是：
|
||||
|
|
@ -38,9 +38,9 @@ $transcription$表示当前文本框的文字，在文本检测任务中并不
|
|||
```
|
||||
cd PaddleOCR/
|
||||
# 下载MobileNetV3的预训练模型
|
||||
wget -P /PaddleOCR/pretrain_models/ 模型链接
|
||||
wget -P /PaddleOCR/pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x0_5_pretrained.tar
|
||||
# 下载ResNet50的预训练模型
|
||||
wget -P /PaddleOCR/pretrain_models/ 模型链接
|
||||
wget -P /PaddleOCR/pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
|
||||
```
|
||||
|
||||
**启动训练**
|
||||
|
|
@ -49,7 +49,7 @@ python3 tools/train.py -c configs/det/det_db_mv3.yml
|
|||
```
|
||||
|
||||
上述指令中，通过-c 选择训练使用configs/det/det_db_mv3.yml配置文件。
|
||||
有关配置文件的详细解释，请参考[链接]()。
|
||||
有关配置文件的详细解释，请参考[链接](./doc/config.md)。
|
||||
|
||||
您也可以通过-o参数在不需要修改yml文件的情况下，改变训练的参数，比如，调整训练的学习率为0.0001
|
||||
```
|
||||
|
|
|
|||
|
|
@ -64,7 +64,7 @@ class CharacterOps(object):
|
|||
[sum(text_lengths)] = [text_index_0 + text_index_1 + ... + text_index_(n - 1)]
|
||||
length: length of each text. [batch_size]
|
||||
"""
|
||||
if self.character_type == "en" or text.encode( 'UTF-8' ).isalpha():
|
||||
if self.character_type == "en":
|
||||
text = text.lower()
|
||||
|
||||
text_list = []
|
||||
|
|
|
|||
|
|
@ -1,3 +1,13 @@
|
|||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
@ -24,13 +34,3 @@ w
|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
|
|||
Loading…
Reference in New Issue