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@ -12,120 +12,35 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
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- [more](./doc/doc_ch/update.md)
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## 特性
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- 超轻量级中文OCR，总模型仅8.6M
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- 超轻量级中文OCR模型，总模型仅8.6M
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- 单模型支持中英文数字组合识别、竖排文本识别、长文本识别
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- 检测模型DB（4.1M）+识别模型CRNN（4.5M）
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- 实用通用中文OCR模型
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- 多种预测推理部署方案，包括服务部署和端测部署
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- 多种文本检测训练算法，EAST、DB
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- 多种文本识别训练算法，Rosetta、CRNN、STAR-Net、RARE
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- 可运行于Linux、Windows、MacOS等多种系统
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<a name="支持的中文模型列表"></a>
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### 支持的中文模型列表:
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|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|
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|-|-|-|-|-|
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|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)
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|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)
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超轻量级中文OCR在线体验地址：https://www.paddlepaddle.org.cn/hub/scene/ocr
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**也可以按如下教程快速体验中文OCR模型。**
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## **超轻量级中文OCR以及通用中文OCR体验**
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## 快速体验
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上图是超轻量级中文OCR模型效果展示，更多效果图请见文末[超轻量级中文OCR效果展示](#超轻量级中文OCR效果展示)、
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[通用中文OCR效果展示](#通用中文OCR效果展示)、[支持空格的中文OCR效果展示](#支持空格的中文OCR效果展示)。
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#### 1.环境配置
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- 超轻量级中文OCR在线体验地址：https://www.paddlepaddle.org.cn/hub/scene/ocr
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请先参考[快速安装](./doc/doc_ch/installation.md)配置PaddleOCR运行环境。
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- [中文OCR模型快速开始](./doc/doc_ch/quickstart.md)
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#### 2.inference模型下载
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## 中文OCR模型列表
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*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*
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|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|
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|-|-|-|-|-|
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|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)
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|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)
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#### 下载检测/识别模型并解压
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复制[中文模型列表](#支持的中文模型列表) 中的检测和识别 `inference模型` 地址，下载并解压：
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```
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mkdir inference && cd inference
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# 下载检测模型并解压
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wget {url/of/detection/inference_model} && tar xf {name/of/detection/inference_model/package}
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# 下载识别模型并解压
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wget {url/of/recognition/inference_model} && tar xf {name/of/recognition/inference_model/package}
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cd ..
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```
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以超轻量级模型为例：
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```
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mkdir inference && cd inference
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# 下载超轻量级中文OCR模型的检测模型并解压
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wget https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar && tar xf ch_det_mv3_db_infer.tar
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# 下载超轻量级中文OCR模型的识别模型并解压
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wget https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar && tar xf ch_rec_mv3_crnn_infer.tar
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cd ..
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```
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解压完毕后应有如下文件结构：
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```
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|-inference
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|-ch_rec_mv3_crnn
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|- model
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|- params
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|-ch_det_mv3_db
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|- model
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|- params
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...
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```
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#### 3.单张图像或者图像集合预测
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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。
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```bash
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# 预测image_dir指定的单张图像
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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/"
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# 预测image_dir指定的图像集合
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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/"
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# 如果想使用CPU进行预测，需设置use_gpu参数为False
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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
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```
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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：
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```
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# 预测image_dir指定的单张图像
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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/"
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```
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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：
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```
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# 预测image_dir指定的单张图像
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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/"
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```
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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[基于预测引擎推理](./doc/doc_ch/inference.md)。
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## 文档教程
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- [快速安装](./doc/doc_ch/installation.md)
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- [文本检测模型训练/评估/预测](./doc/doc_ch/detection.md)
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- [文本识别模型训练/评估/预测](./doc/doc_ch/recognition.md)
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- [基于预测引擎推理](./doc/doc_ch/inference.md)
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- [yml配置文件参数介绍](./doc/doc_ch/config_ch.md)
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- [数据集](./doc/doc_ch/datasets.md)
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- [FAQ](#FAQ)
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- [联系我们](#欢迎加入PaddleOCR技术交流群)
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- [参考文献](#参考文献)
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## 文本检测算法
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## 算法介绍
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### 1.文本检测算法
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PaddleOCR开源的文本检测算法列表：
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- [x] EAST([paper](https://arxiv.org/abs/1704.03155))
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@ -151,7 +66,7 @@ PaddleOCR开源的文本检测算法列表：
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PaddleOCR文本检测算法的训练和使用请参考文档教程中[文本检测模型训练/评估/预测](./doc/doc_ch/detection.md)。
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## 文本识别算法
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### 2.文本识别算法
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PaddleOCR开源的文本识别算法列表：
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- [x] CRNN([paper](https://arxiv.org/abs/1507.05717))
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PaddleOCR文本识别算法的训练和使用请参考文档教程中[文本识别模型训练/评估/预测](./doc/doc_ch/recognition.md)。
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## 端到端OCR算法
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### 3.端到端OCR算法
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- [ ] [End2End-PSL](https://arxiv.org/abs/1909.07808)(百度自研, comming soon)
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<a name="超轻量级中文OCR效果展示"></a>
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## 超轻量级中文OCR效果展示
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## 文档教程
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- [快速安装](./doc/doc_ch/installation.md)
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- [中文OCR模型快速开始](./doc/doc_ch/quickstart.md)
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- 模型训练/评估/预测
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- [文本检测](./doc/doc_ch/detection.md)
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- [文本识别](./doc/doc_ch/recognition.md)
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- [yml参数配置文件介绍](./doc/doc_ch/config_ch.md)
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- 预测部署
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- [基于Python预测引擎推理](./doc/doc_ch/inference.md)
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- 基于C++预测引擎推理(comming soon)
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- [服务部署](./doc/doc_ch/serving.md)
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- 端测部署(comming soon)
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- [数据集](./doc/doc_ch/datasets.md)
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- [FAQ](#FAQ)
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- 效果展示
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- [超轻量级中文OCR效果展示](#超轻量级中文OCR效果展示)
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- [通用中文OCR效果展示](#通用中文OCR效果展示)
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- [支持空格的中文OCR效果展示](#支持空格的中文OCR效果展示)
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- [技术交流群](#欢迎加入PaddleOCR技术交流群)
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- [参考文献](./doc/doc_ch/reference.md)
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- [许可证书](#许可证书)
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- [贡献代码](#贡献代码)
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## 效果展示
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<a name="超轻量级中文OCR效果展示"></a>
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### 1.超轻量级中文OCR效果展示
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<a name="通用中文OCR效果展示"></a>
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## 通用中文OCR效果展示
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### 2.通用中文OCR效果展示
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<a name="支持空格的中文OCR效果展示"></a>
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## 支持空格的中文OCR效果展示
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### 3.支持空格的中文OCR效果展示
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### 轻量级模型
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### 通用模型
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<a name="FAQ"></a>
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@ -232,65 +159,11 @@ PaddleOCR文本识别算法的训练和使用请参考文档教程中[文本识
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扫描二维码或者加微信：paddlehelp，备注OCR，小助手拉你进群～
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<img src="./doc/paddlehelp.jpg" width = "200" height = "200" />
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<a name="参考文献"></a>
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## 参考文献
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```
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1. EAST:
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@inproceedings{zhou2017east,
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title={EAST: an efficient and accurate scene text detector},
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author={Zhou, Xinyu and Yao, Cong and Wen, He and Wang, Yuzhi and Zhou, Shuchang and He, Weiran and Liang, Jiajun},
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booktitle={Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},
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pages={5551--5560},
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year={2017}
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}
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2. DB:
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@article{liao2019real,
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title={Real-time Scene Text Detection with Differentiable Binarization},
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author={Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang},
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journal={arXiv preprint arXiv:1911.08947},
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year={2019}
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}
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3. DTRB:
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@inproceedings{baek2019wrong,
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title={What is wrong with scene text recognition model comparisons? dataset and model analysis},
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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},
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booktitle={Proceedings of the IEEE International Conference on Computer Vision},
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pages={4715--4723},
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year={2019}
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}
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4. SAST:
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@inproceedings{wang2019single,
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title={A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning},
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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},
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booktitle={Proceedings of the 27th ACM International Conference on Multimedia},
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pages={1277--1285},
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year={2019}
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}
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5. SRN:
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@article{yu2020towards,
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title={Towards Accurate Scene Text Recognition with Semantic Reasoning Networks},
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author={Yu, Deli and Li, Xuan and Zhang, Chengquan and Han, Junyu and Liu, Jingtuo and Ding, Errui},
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journal={arXiv preprint arXiv:2003.12294},
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year={2020}
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}
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6. end2end-psl:
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@inproceedings{sun2019chinese,
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title={Chinese Street View Text: Large-scale Chinese Text Reading with Partially Supervised Learning},
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author={Sun, Yipeng and Liu, Jiaming and Liu, Wei and Han, Junyu and Ding, Errui and Liu, Jingtuo},
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booktitle={Proceedings of the IEEE International Conference on Computer Vision},
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pages={9086--9095},
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year={2019}
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}
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```
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<a name="许可证书"></a>
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## 许可证书
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本项目的发布受<a href="https://github.com/PaddlePaddle/PaddleOCR/blob/master/LICENSE">Apache 2.0 license</a>许可认证。
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<a name="贡献代码"></a>
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## 贡献代码
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我们非常欢迎你为PaddleOCR贡献代码，也十分感谢你的反馈。
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@ -0,0 +1,81 @@
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# 中文OCR模型快速开始
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## 1.环境配置
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请先参考[快速安装](./installation.md)配置PaddleOCR运行环境。
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## 2.inference模型下载
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|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|
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|-|-|-|-|-|
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|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[基于预测引擎推理](./inference.md)。
|
||||
|
|
@ -0,0 +1,55 @@
|
|||
# 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}
|
||||
}
|
||||
```
|
||||
|
|
@ -0,0 +1,31 @@
|
|||
# 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
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
<a name="通用中文OCR"></a>
|
||||
## 通用中文OCR效果展示
|
||||
|
||||
|
||||
|
||||
|
||||
<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
|
||||
|
||||
|
||||
### 通用模型
|
||||
|
||||
Loading…
Reference in New Issue