Merge pull request #302 from dyning/develop

Updata readme

doc/doc_ch/inference.md

@@ -1,5 +1,5 @@
 
-# 基于预测引擎推理
+# 基于Python预测引擎推理
 
 inference 模型（fluid.io.save_inference_model保存的模型）
 一般是模型训练完成后保存的固化模型，多用于预测部署。

doc/doc_ch/quickstart.md

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+
+# 中文OCR模型快速开始
+
+## 1.环境配置
+
+请先参考[快速安装](./installation.md)配置PaddleOCR运行环境。
+
+## 2.inference模型下载
+
+|模型名称|模型简介|检测模型地址|识别模型地址|支持空格的识别模型地址|
+|-|-|-|-|-|
+|chinese_db_crnn_mobile|超轻量级中文OCR模型|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance.tar)
+|chinese_db_crnn_server|通用中文OCR模型|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance.tar)
+
+*windows 环境下如果没有安装wget,下载模型时可将链接复制到浏览器中下载，并解压放置在相应目录下*
+
+复制上表中的检测和识别的`inference模型`下载地址，并解压
+
+```
+mkdir inference && cd inference
+# 下载检测模型并解压
+wget {url/of/detection/inference_model} && tar xf {name/of/detection/inference_model/package}
+# 下载识别模型并解压
+wget {url/of/recognition/inference_model} && tar xf {name/of/recognition/inference_model/package}
+cd ..
+```
+
+以超轻量级模型为例：
+
+```
+mkdir inference && cd inference
+# 下载超轻量级中文OCR模型的检测模型并解压
+wget https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar && tar xf ch_det_mv3_db_infer.tar
+# 下载超轻量级中文OCR模型的识别模型并解压
+wget https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar && tar xf ch_rec_mv3_crnn_infer.tar
+cd ..
+```
+
+解压完毕后应有如下文件结构：
+
+```
+|-inference
+    |-ch_rec_mv3_crnn
+        |- model
+        |- params
+    |-ch_det_mv3_db
+        |- model
+        |- params
+    ...
+```
+
+## 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。
+
+```bash
+
+# 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/ch_det_mv3_db/"  --rec_model_dir="./inference/ch_rec_mv3_crnn/"
+
+# 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/ch_det_mv3_db/"  --rec_model_dir="./inference/ch_rec_mv3_crnn/"
+
+# 如果想使用CPU进行预测，需设置use_gpu参数为False
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_det_mv3_db/"  --rec_model_dir="./inference/ch_rec_mv3_crnn/" --use_gpu=False
+```
+
+通用中文OCR模型的体验可以按照上述步骤下载相应的模型，并且更新相关的参数，示例如下：
+```
+# 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/ch_det_r50_vd_db/"  --rec_model_dir="./inference/ch_rec_r34_vd_crnn/"
+```
+
+带空格的通用中文OCR模型的体验可以按照上述步骤下载相应的模型，并且更新相关的参数，示例如下：
+
+```
+# 预测image_dir指定的单张图像
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs_en/img_12.jpg" --det_model_dir="./inference/ch_det_r50_vd_db/"  --rec_model_dir="./inference/ch_rec_r34_vd_crnn_enhance/"
+```
+
+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Python预测引擎推理](./inference.md)。

doc/doc_ch/reference.md

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+# 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}
+}
+```

doc/doc_ch/update.md

@@ -1,5 +1,7 @@
 # 版本更新
-
+- 2020.7.9 添加支持空格的识别模型，识别效果
+- 2020.7.9 添加数据增强、学习率衰减策略,具体参考配置文件
+- 2020.6.8 添加数据集，并保持持续更新
 - 2020.6.5 支持 `attetnion` 模型导出 `inference_model`
 - 2020.6.5 支持单独预测识别时，输出结果得分
 - 2020.5.30 提供超轻量级中文OCR在线体验

doc/doc_ch/visualization.md

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+# 效果展示
+- [超轻量级中文OCR效果展示](#超轻量级中文OCR)
+- [通用中文OCR效果展示](#通用中文OCR)
+- [支持空格的中文OCR效果展示](#支持空格的中文OCR)
+
+<a name="超轻量级中文OCR"></a>
+## 超轻量级中文OCR效果展示
+
+![](../imgs_results/1.jpg)
+![](../imgs_results/7.jpg)
+![](../imgs_results/12.jpg)
+![](../imgs_results/4.jpg)
+![](../imgs_results/6.jpg)
+![](../imgs_results/9.jpg)
+![](../imgs_results/16.png)
+![](../imgs_results/22.jpg)
+
+<a name="通用中文OCR"></a>
+## 通用中文OCR效果展示
+![](../imgs_results/chinese_db_crnn_server/11.jpg)
+![](../imgs_results/chinese_db_crnn_server/2.jpg)
+![](../imgs_results/chinese_db_crnn_server/8.jpg)
+
+<a name="支持空格的中文OCR"></a>
+## 支持空格的中文OCR效果展示
+
+### 轻量级模型
+![](../imgs_results/img_11.jpg)
+
+### 通用模型
+![](../imgs_results/chinese_db_crnn_server/en_paper.jpg)