From 561c54460c24a0d08ee9d6c5ea4d7daf535b2aff Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Tue, 12 May 2020 15:04:33 +0800 Subject: [PATCH] update readme and doc --- README.md | 126 +++++++++++++++++++++++++++++++++++++++++++- doc/detection.md | 6 +-- doc/installation.md | 14 ++++- 3 files changed, 140 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 4d6fb7fb..60ab666d 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,124 @@ -# PaddleOCR -OCR algorithms with PaddlePaddle （still under develop) + +# 简介 +PaddleOCR旨在打造一套丰富、领先、且实用的文字检测、识别模型/工具库，助力使用者训练出更好的模型，并应用落地。 + +【这里加上效果图】 + +## 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) + +## 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 + - (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 + +## 文本检测算法: + +PaddleOCR提供的文本检测算法列表： +- [EAST](https://arxiv.org/abs/1704.03155) +- [DB](https://arxiv.org/abs/1911.08947) +- [SAST](https://arxiv.org/abs/1908.05498) + +算法效果： +|模型|骨干网络|数据集|Hmean| +|-|-|-|-| +|EAST|ResNet50_vd|ICDAR2015|85.85%| +|EAST|MobileNetV3|ICDAR2015|79.08%| +|DB|ResNet50_vd|ICDAR2015|83.30%| +|DB|MobileNetV3|ICDAR2015|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提供的文本识别算法列表： +- (CRNN)[https://arxiv.org/abs/1507.05717] +- [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： + +以下指标是在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上的评测结果的平均。 + +|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)。 + +## 端到端算法 +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 +``` +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} +} + +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} +} + +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} +} + +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} +} + +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} +} + +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} +} +``` diff --git a/doc/detection.md b/doc/detection.md index 03052b76..f4295177 100644 --- a/doc/detection.md +++ b/doc/detection.md @@ -35,15 +35,15 @@ json.dumps编码前的图像标注信息是包含多个字典的list，字典中 首先下载pretrain model，PaddleOCR的检测模型目前支持两种backbone，分别是MobileNetV3、ResNet50_vd， 您可以根据需求使用[PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/master/ppcls/modeling/architectures)中的模型更换backbone。 ``` +cd PaddleOCR/ # 下载MobileNetV3的预训练模型 -wget -P /PaddleOCR/pretrained_model/ 模型链接 +wget -P /PaddleOCR/pretrain_models/ 模型链接 # 下载ResNet50的预训练模型 -wget -P /PaddleOCR/pretrained_model/ 模型链接 +wget -P /PaddleOCR/pretrain_models/ 模型链接 ``` **启动训练** ``` -cd PaddleOCR/ python3 tools/train.py -c configs/det/det_db_mv3.yml ``` diff --git a/doc/installation.md b/doc/installation.md index b190b3e3..5fca344e 100644 --- a/doc/installation.md +++ b/doc/installation.md @@ -1,12 +1,17 @@ -### 2.1 快速安装 +## 快速安装 建议使用我们提供的docker运行PaddleOCR，有关docker使用请参考[链接](https://docs.docker.com/get-started/)。 1. 准备docker环境。第一次使用这个镜像，会自动下载该镜像，请耐心等待。 ``` # 切换到工作目录下 cd /home/Projects +# 首次运行需创建一个docker容器，再次运行时不需要运行当前命令 # 创建一个名字为pdocr的docker容器，并将当前目录映射到容器的/data目录下 sudo nvidia-docker run --name pdocr -v $PWD:/data --network=host -it hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev /bin/bash + +# ctrl+P+Q可退出docker，重新进入docker使用如下命令 +sudo nvidia-docker container exec -it pdocr /bin/bash + ``` 2. 克隆PaddleOCR repo代码 @@ -20,3 +25,10 @@ cd PaddleOCR pip3 install --upgrade pip pip3 install -r requirements.txt ``` + +## 快速运行 + +``` +python3 tools/infer/predict_eval.py --image_file="./" +``` +【可视化运行结果】 -- GitLab