Update doc

README.md

@@ -1,2 +1,149 @@
-# PaddleOCR
-OCR algorithms with PaddlePaddle （still under develop)
+
+# 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](./doc/installation.md)
+- [文本识别模型训练/评估/预测](./doc/detection.md)
+- [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/ 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](https://paddleocr.bj.bcebos.com/det_r50_vd_east.tar)|85.85%|
+|EAST|[MobileNetV3](https://paddleocr.bj.bcebos.com/det_mv3_east.tar)|79.08%|
+|DB|[ResNet50_vd](https://paddleocr.bj.bcebos.com/det_r50_vd_db.tar)|83.30%|
+|DB|[MobileNetV3](https://paddleocr.bj.bcebos.com/det_mv3_db.tar)|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](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_none_ctc.tar)|80.24%|
+|Rosetta|[MobileNetV3](https://paddleocr.bj.bcebos.com/rec_mv3_none_none_ctc.tar)|78.16%|
+|CRNN|[Resnet34_vd](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_bilstm_ctc.tar)|82.20%|
+|CRNN|[MobileNetV3](https://paddleocr.bj.bcebos.com/rec_mv3_none_bilstm_ctc.tar)|79.37%|
+|STAR-Net|[Resnet34_vd](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_ctc.tar)|83.93%|
+|STAR-Net|[MobileNetV3](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_ctc.tar)|81.56%|
+|RARE|[Resnet34_vd](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_attn.tar)|84.90%|
+|RARE|[MobileNetV3](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_attn.tar)|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}
+}
+```

configs/rec/rec_chinese_lite_train.yml

@@ -0,0 +1,42 @@
+Global:
+  algorithm: CRNN
+  use_gpu: true
+  epoch_num: 3000
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_CRNN
+  save_epoch_step: 3
+  eval_batch_step: 2000
+  train_batch_size_per_card: 256
+  test_batch_size_per_card: 256
+  image_shape: [3, 32, 100]
+  max_text_length: 25
+  character_type: ch
+  character_dict_path: ./ppocr/utils/ppocr_keys_v1.txt
+  loss_type: ctc
+  reader_yml: ./configs/rec/rec_chinese_reader.yml
+  pretrain_weights: ./pretrain_models/CRNN/best_accuracy 
+  checkpoints:
+  save_inference_dir:
+Architecture:
+  function: ppocr.modeling.architectures.rec_model,RecModel
+
+Backbone:
+  function: ppocr.modeling.backbones.rec_mobilenet_v3,MobileNetV3
+  scale: 0.5
+  model_name: small
+
+Head:
+  function: ppocr.modeling.heads.rec_ctc_head,CTCPredict
+  encoder_type: rnn
+  SeqRNN:
+    hidden_size: 48
+
+Loss:
+  function: ppocr.modeling.losses.rec_ctc_loss,CTCLoss
+
+Optimizer:
+  function: ppocr.optimizer,AdamDecay
+  base_lr: 0.0005
+  beta1: 0.9
+  beta2: 0.999

configs/rec/rec_chinese_reader.yml

@@ -0,0 +1,14 @@
+TrainReader:
+  reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
+  num_workers: 8
+  img_set_dir: ./train_data
+  label_file_path: ./train_data/rec_gt_train.txt
+
+EvalReader:
+  reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
+  img_set_dir: ./train_data
+  label_file_path: ./train_data/rec_gt_test.txt
+
+TestReader:
+  reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
+  infer_img: ./infer_img

configs/rec/rec_icdar15_train.yml

@@ -11,8 +11,8 @@ Global:
   test_batch_size_per_card: 256
   image_shape: [3, 32, 100]
   max_text_length: 25
-  character_type: ch
-  character_dict_path: ./ppocr/utils/ic15_dict.txt
+  character_type: en
+  character_dict_path: /workspace/PaddleOCR/train_data/ic15_dict.txt
   loss_type: ctc
   reader_yml: ./configs/rec/rec_icdar15_reader.yml
   pretrain_weights: ./pretrain_models/CRNN/best_accuracy 
@@ -24,13 +24,13 @@ Architecture:
 Backbone:
   function: ppocr.modeling.backbones.rec_mobilenet_v3,MobileNetV3
   scale: 0.5
-  model_name: small
+  model_name: large
 
 Head:
   function: ppocr.modeling.heads.rec_ctc_head,CTCPredict
   encoder_type: rnn
   SeqRNN:
-    hidden_size: 48
+    hidden_size: 96
 
 Loss:
   function: ppocr.modeling.losses.rec_ctc_loss,CTCLoss