remove README for now

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}
-}
-```
+# PaddleOCR
+OCR algorithms with PaddlePaddle （still under develop)

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}
+}
+```