Merge pull request #9 from tink2123/update_doc

Update doc

Merge pull request #9 from tink2123/update_doc
Update doc
3954d466 · dyning · GitHub · c01e3267 · 0f578ae7 · 3954d466
Unverified Commit 3954d466 authored May 14, 2020 by dyning Committed by GitHub May 14, 2020
9 changed files
--- a/README.md
+++ b/README.md
-# 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}
+}
+```
--- a/configs/rec/rec_chinese_lite_train.yml
+++ b/configs/rec/rec_chinese_lite_train.yml
+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
--- a/configs/rec/rec_chinese_reader.yml
+++ b/configs/rec/rec_chinese_reader.yml
+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
--- a/configs/rec/rec_icdar15_train.yml
+++ b/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_type: en
-  character_dict_path: ./ppocr/utils/ic15_dict.txt
+  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

--- a/doc/README.md
+++ b/doc/README.md
-# 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
-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：
- [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}
-}
-```
--- a/doc/config.md
+++ b/doc/config.md
+# 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 |
--- a/doc/detection.md
+++ b/doc/detection.md
@@ -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/  https://paddleocr.bj.bcebos.com/dataset%2Ftrain_icdar2015_label.txt
-wget -P /PaddleOCR/train_data/  测试标注文件链接
+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
 ```

--- a/ppocr/utils/character.py
+++ b/ppocr/utils/character.py
@@ -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 = []

--- a/ppocr/utils/ic15_dict.txt
+++ b/ppocr/utils/ic15_dict.txt
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 a
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@@ -24,13 +34,3 @@ w
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