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include LICENSE.txt
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[English](README.md) | 简体中文
## 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。
**近期更新**
- 2020.8.16 开源文本检测算法[SAST](https://arxiv.org/abs/1908.05498)和文本识别算法[SRN](https://arxiv.org/abs/2003.12294)
- 2020.7.23 发布7月21日B站直播课回放和PPT，PaddleOCR开源大礼包全面解读，[获取地址](https://aistudio.baidu.com/aistudio/course/introduce/1519)
- 2020.7.15 添加基于EasyEdge和Paddle-Lite的移动端DEMO，支持iOS和Android系统
- 2020.7.15 完善预测部署，添加基于C++预测引擎推理、服务化部署和端侧部署方案，以及超轻量级中文OCR模型预测耗时Benchmark
- 2020.7.15 整理OCR相关数据集、常用数据标注以及合成工具
- [more](./doc/doc_ch/update.md)
## 特性
- 超轻量级中文OCR模型，总模型仅8.6M
- 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
- 检测模型DB（4.1M）+识别模型CRNN（4.5M）
- 实用通用中文OCR模型
- 多种预测推理部署方案，包括服务部署和端侧部署
- 多种文本检测训练算法，EAST、DB
- 多种文本识别训练算法，Rosetta、CRNN、STAR-Net、RARE
- 可运行于Linux、Windows、MacOS等多种系统
## 快速体验
<div align="center">
<img src="doc/imgs_results/11.jpg" width="800">
</div>
上图是超轻量级中文OCR模型效果展示，更多效果图请见[效果展示页面](./doc/doc_ch/visualization.md)。
- 超轻量级中文OCR在线体验地址：https://www.paddlepaddle.org.cn/hub/scene/ocr
- 移动端DEMO体验(基于EasyEdge和Paddle-Lite, 支持iOS和Android系统)：[安装包二维码获取地址](https://ai.baidu.com/easyedge/app/openSource?from=paddlelite)
Android手机也可以扫描下面二维码安装体验。
<div align="center">
<img src="./doc/ocr-android-easyedge.png" width = "200" height = "200" />
</div>
- [**中文OCR模型快速使用**](./doc/doc_ch/quickstart.md)
## 中文OCR模型列表
|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)
## 文档教程
- [快速安装](./doc/doc_ch/installation.md)
- [中文OCR模型快速使用](./doc/doc_ch/quickstart.md)
- 算法介绍
- [文本检测](#文本检测算法)
- [文本识别](#文本识别算法)
- [端到端OCR](#端到端OCR算法)
- 模型训练/评估
- [文本检测](./doc/doc_ch/detection.md)
- [文本识别](./doc/doc_ch/recognition.md)
- [yml参数配置文件介绍](./doc/doc_ch/config.md)
- [中文OCR训练预测技巧](./doc/doc_ch/tricks.md)
- 预测部署
- [基于Python预测引擎推理](./doc/doc_ch/inference.md)
- [基于C++预测引擎推理](./deploy/cpp_infer/readme.md)
- [服务化部署](./doc/doc_ch/serving.md)
- [端侧部署](./deploy/lite/readme.md)
- 模型量化压缩（coming soon）
- [Benchmark](./doc/doc_ch/benchmark.md)
- 数据集
- [通用中英文OCR数据集](./doc/doc_ch/datasets.md)
- [手写中文OCR数据集](./doc/doc_ch/handwritten_datasets.md)
- [垂类多语言OCR数据集](./doc/doc_ch/vertical_and_multilingual_datasets.md)
- [常用数据标注工具](./doc/doc_ch/data_annotation.md)
- [常用数据合成工具](./doc/doc_ch/data_synthesis.md)
- [FAQ](#FAQ)
- 效果展示
- [超轻量级中文OCR效果展示](#超轻量级中文OCR效果展示)
- [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)
- [技术交流群](#欢迎加入PaddleOCR技术交流群)
- [参考文献](./doc/doc_ch/reference.md)
- [许可证书](#许可证书)
- [贡献代码](#贡献代码)
<a name="算法介绍"></a>
## 算法介绍
<a name="文本检测算法"></a>
### 1.文本检测算法
PaddleOCR开源的文本检测算法列表：
- [x] EAST([paper](https://arxiv.org/abs/1704.03155))
- [x] DB([paper](https://arxiv.org/abs/1911.08947))
- [x] SAST([paper](https://arxiv.org/abs/1908.05498))(百度自研)
在ICDAR2015文本检测公开数据集上，算法效果如下：
|模型|骨干网络|precision|recall|Hmean|下载链接|
|-|-|-|-|-|-|
|EAST|ResNet50_vd|88.18%|85.51%|86.82%|[下载链接](https://paddleocr.bj.bcebos.com/det_r50_vd_east.tar)|
|EAST|MobileNetV3|81.67%|79.83%|80.74%|[下载链接](https://paddleocr.bj.bcebos.com/det_mv3_east.tar)|
|DB|ResNet50_vd|83.79%|80.65%|82.19%|[下载链接](https://paddleocr.bj.bcebos.com/det_r50_vd_db.tar)|
|DB|MobileNetV3|75.92%|73.18%|74.53%|[下载链接](https://paddleocr.bj.bcebos.com/det_mv3_db.tar)|
|SAST|ResNet50_vd|92.18%|82.96%|87.33%|[下载链接](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_icdar2015.tar)|
在Total-text文本检测公开数据集上，算法效果如下：
|模型|骨干网络|precision|recall|Hmean|下载链接|
|-|-|-|-|-|-|
|SAST|ResNet50_vd|88.74%|79.80%|84.03%|[下载链接](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_total_text.tar)|
**说明：** SAST模型训练额外加入了icdar2013、icdar2017、COCO-Text、ArT等公开数据集进行调优。PaddleOCR用到的经过整理格式的英文公开数据集下载：[百度云地址](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (提取码: 2bpi)
使用[LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/datasets.md#1icdar2019-lsvt)街景数据集共3w张数据，训练中文检测模型的相关配置和预训练文件如下：
|模型|骨干网络|配置文件|预训练模型|
|-|-|-|-|
|超轻量中文模型|MobileNetV3|det_mv3_db.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|
|通用中文OCR模型|ResNet50_vd|det_r50_vd_db.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|
* 注： 上述DB模型的训练和评估，需设置后处理参数box_thresh=0.6，unclip_ratio=1.5，使用不同数据集、不同模型训练，可调整这两个参数进行优化
PaddleOCR文本检测算法的训练和使用请参考文档教程中[模型训练/评估中的文本检测部分](./doc/doc_ch/detection.md)。
<a name="文本识别算法"></a>
### 2.文本识别算法
PaddleOCR开源的文本识别算法列表：
- [x] CRNN([paper](https://arxiv.org/abs/1507.05717))
- [x] Rosetta([paper](https://arxiv.org/abs/1910.05085))
- [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))
- [x] RARE([paper](https://arxiv.org/abs/1603.03915v1))
- [x] SRN([paper](https://arxiv.org/abs/2003.12294))(百度自研)
参考[DTRB](https://arxiv.org/abs/1904.01906)文字识别训练和评估流程，使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法效果如下：
|模型|骨干网络|Avg Accuracy|模型存储命名|下载链接|
|-|-|-|-|-|
|Rosetta|Resnet34_vd|80.24%|rec_r34_vd_none_none_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_none_ctc.tar)|
|Rosetta|MobileNetV3|78.16%|rec_mv3_none_none_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_none_none_ctc.tar)|
|CRNN|Resnet34_vd|82.20%|rec_r34_vd_none_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_bilstm_ctc.tar)|
|CRNN|MobileNetV3|79.37%|rec_mv3_none_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_none_bilstm_ctc.tar)|
|STAR-Net|Resnet34_vd|83.93%|rec_r34_vd_tps_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_ctc.tar)|
|STAR-Net|MobileNetV3|81.56%|rec_mv3_tps_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_ctc.tar)|
|RARE|Resnet34_vd|84.90%|rec_r34_vd_tps_bilstm_attn|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_attn.tar)|
|RARE|MobileNetV3|83.32%|rec_mv3_tps_bilstm_attn|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_attn.tar)|
|SRN|Resnet50_vd_fpn|88.33%|rec_r50fpn_vd_none_srn|[下载链接](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar)|
**说明：** SRN模型使用了数据扰动方法对上述提到对两个训练集进行增广，增广后的数据可以在[百度网盘](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA)上下载，提取码: y3ry。
原始论文使用两阶段训练平均精度为89.74%，PaddleOCR中使用one-stage训练，平均精度为88.33%。两种预训练权重均在[下载链接](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar)中。
使用[LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/datasets.md#1icdar2019-lsvt)街景数据集根据真值将图crop出来30w数据，进行位置校准。此外基于LSVT语料生成500w合成数据训练中文模型，相关配置和预训练文件如下：
|模型|骨干网络|配置文件|预训练模型|
|-|-|-|-|
|超轻量中文模型|MobileNetV3|rec_chinese_lite_train.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|
|通用中文OCR模型|Resnet34_vd|rec_chinese_common_train.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|
PaddleOCR文本识别算法的训练和使用请参考文档教程中[模型训练/评估中的文本识别部分](./doc/doc_ch/recognition.md)。
<a name="端到端OCR算法"></a>
### 3.端到端OCR算法
- [ ] [End2End-PSL](https://arxiv.org/abs/1909.07808)(百度自研, coming soon)
## 效果展示
<a name="超轻量级中文OCR效果展示"></a>
### 1.超轻量级中文OCR效果展示 [more](./doc/doc_ch/visualization.md)
<div align="center">
<img src="doc/imgs_results/1.jpg" width="800">
</div>
<a name="通用中文OCR效果展示"></a>
### 2.通用中文OCR效果展示 [more](./doc/doc_ch/visualization.md)
<div align="center">
<img src="doc/imgs_results/chinese_db_crnn_server/11.jpg" width="800">
</div>
<a name="支持空格的中文OCR效果展示"></a>
### 3.支持空格的中文OCR效果展示 [more](./doc/doc_ch/visualization.md)
<div align="center">
<img src="doc/imgs_results/chinese_db_crnn_server/en_paper.jpg" width="800">
</div>
<a name="FAQ"></a>
## FAQ
1. **转换attention识别模型时报错：KeyError: 'predict'**
问题已解，请更新到最新代码。
2. **关于推理速度**
图片中的文字较多时，预测时间会增，可以使用--rec_batch_num设置更小预测batch num，默认值为30，可以改为10或其他数值。
3. **服务部署与移动端部署**
预计6月中下旬会先后发布基于Serving的服务部署方案和基于Paddle Lite的移动端部署方案，欢迎持续关注。
4. **自研算法发布时间**
自研算法SAST、SRN、End2End-PSL都将在7-8月陆续发布，敬请期待。
[more](./doc/doc_ch/FAQ.md)
<a name="欢迎加入PaddleOCR技术交流群"></a>
## 欢迎加入PaddleOCR技术交流群
请扫描下面二维码，完成问卷填写，获取加群二维码和OCR方向的炼丹秘籍
<div align="center">
<img src="./doc/joinus.jpg" width = "200" height = "200" />
</div>
<a name="许可证书"></a>
## 许可证书
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。
<a name="贡献代码"></a>
## 贡献代码
我们非常欢迎你为PaddleOCR贡献代码，也十分感谢你的反馈。
- 非常感谢 [Khanh Tran](https://github.com/xxxpsyduck) 和 [Karl Horky](https://github.com/karlhorky) 贡献修改英文文档
- 非常感谢 [zhangxin](https://github.com/ZhangXinNan)([Blog](https://blog.csdn.net/sdlypyzq)) 贡献新的可视化方式、添加.gitgnore、处理手动设置PYTHONPATH环境变量的问题
- 非常感谢 [lyl120117](https://github.com/lyl120117) 贡献打印网络结构的代码
- 非常感谢 [xiangyubo](https://github.com/xiangyubo) 贡献手写中文OCR数据集
- 非常感谢 [authorfu](https://github.com/authorfu) 贡献Android和[xiadeye](https://github.com/xiadeye) 贡献IOS的demo代码
- 非常感谢 [BeyondYourself](https://github.com/BeyondYourself) 给PaddleOCR提了很多非常棒的建议，并简化了PaddleOCR的部分代码风格。
- 非常感谢 [tangmq](https://gitee.com/tangmq) 给PaddleOCR增加Docker化部署服务，支持快速发布可调用的Restful API服务。
README_en.md 0 → 100644
浏览文件 @ 5fc9fff9
English | [简体中文](README.md)
## Introduction
PaddleOCR aims to create rich, leading, and practical OCR tools that help users train better models and apply them into practice.
**Recent updates**
- 2020.8.24 Support the use of PaddleOCR through whl package installation，pelease refer [PaddleOCR Package](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/whl_en.md)
- 2020.8.16, Release text detection algorithm [SAST](https://arxiv.org/abs/1908.05498) and text recognition algorithm [SRN](https://arxiv.org/abs/2003.12294)
- 2020.7.23, Release the playback and PPT of live class on BiliBili station, PaddleOCR Introduction, [address](https://aistudio.baidu.com/aistudio/course/introduce/1519)
- 2020.7.15, Add mobile App demo , support both iOS and Android ( based on easyedge and Paddle Lite)
- 2020.7.15, Improve the deployment ability, add the C + + inference , serving deployment. In addition, the benchmarks of the ultra-lightweight OCR model are provided.
- 2020.7.15, Add several related datasets, data annotation and synthesis tools.
- [more](./doc/doc_en/update_en.md)
## Features
- Ultra-lightweight OCR model, total model size is only 8.6M
- Single model supports Chinese/English numbers combination recognition, vertical text recognition, long text recognition
- Detection model DB (4.1M) + recognition model CRNN (4.5M)
- Various text detection algorithms: EAST, DB
- Various text recognition algorithms: Rosetta, CRNN, STAR-Net, RARE
- Support Linux, Windows, macOS and other systems.
## Visualization
![](doc/imgs_results/11.jpg)
![](doc/imgs_results/img_10.jpg)
[More visualization](./doc/doc_en/visualization_en.md)
You can also quickly experience the ultra-lightweight OCR : [Online Experience](https://www.paddlepaddle.org.cn/hub/scene/ocr)
Mobile DEMO experience (based on EasyEdge and Paddle-Lite, supports iOS and Android systems): [Sign in to the website to obtain the QR code for installing the App](https://ai.baidu.com/easyedge/app/openSource?from=paddlelite)
Also, you can scan the QR code below to install the App (**Android support only**)
<div align="center">
<img src="./doc/ocr-android-easyedge.png" width = "200" height = "200" />
</div>
- [**OCR Quick Start**](./doc/doc_en/quickstart_en.md)
<a name="Supported-Chinese-model-list"></a>
### Supported Models:
|Model Name|Description |Detection Model link|Recognition Model link| Support for space Recognition Model link|
|-|-|-|-|-|
|db_crnn_mobile|ultra-lightweight OCR model|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar) / [pre-train model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance.tar)
|db_crnn_server|General OCR model|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance_infer.tar) / [pre-train model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance.tar)
## Tutorials
- [Installation](./doc/doc_en/installation_en.md)
- [Quick Start](./doc/doc_en/quickstart_en.md)
- Algorithm introduction
- [Text Detection Algorithm](#TEXTDETECTIONALGORITHM)
- [Text Recognition Algorithm](#TEXTRECOGNITIONALGORITHM)
- Model training/evaluation
- [Text Detection](./doc/doc_en/detection_en.md)
- [Text Recognition](./doc/doc_en/recognition_en.md)
- [Yml Configuration](./doc/doc_en/config_en.md)
- [Tricks](./doc/doc_en/tricks_en.md)
- Deployment
- [Python Inference](./doc/doc_en/inference_en.md)
- [C++ Inference](./deploy/cpp_infer/readme_en.md)
- [Serving](./doc/doc_en/serving_en.md)
- [Mobile](./deploy/lite/readme_en.md)
- Model Quantization and Compression (coming soon)
- [Benchmark](./doc/doc_en/benchmark_en.md)
- Datasets
- [General OCR Datasets(Chinese/English)](./doc/doc_en/datasets_en.md)
- [HandWritten_OCR_Datasets(Chinese)](./doc/doc_en/handwritten_datasets_en.md)
- [Various OCR Datasets(multilingual)](./doc/doc_en/vertical_and_multilingual_datasets_en.md)
- [Data Annotation Tools](./doc/doc_en/data_annotation_en.md)
- [Data Synthesis Tools](./doc/doc_en/data_synthesis_en.md)
- [FAQ](#FAQ)
- Visualization
- [Ultra-lightweight Chinese/English OCR Visualization](#UCOCRVIS)
- [General Chinese/English OCR Visualization](#GeOCRVIS)
- [Chinese/English OCR Visualization (Support Space Recognition )](#SpaceOCRVIS)
- [Community](#Community)
- [References](./doc/doc_en/reference_en.md)
- [License](#LICENSE)
- [Contribution](#CONTRIBUTION)
<a name="TEXTDETECTIONALGORITHM"></a>
## Text Detection Algorithm
PaddleOCR open source text detection algorithms list:
- [x] EAST([paper](https://arxiv.org/abs/1704.03155))
- [x] DB([paper](https://arxiv.org/abs/1911.08947))
- [x] SAST([paper](https://arxiv.org/abs/1908.05498))(Baidu Self-Research)
On the ICDAR2015 dataset, the text detection result is as follows:
|Model|Backbone|precision|recall|Hmean|Download link|
|-|-|-|-|-|-|
|EAST|ResNet50_vd|88.18%|85.51%|86.82%|[Download link](https://paddleocr.bj.bcebos.com/det_r50_vd_east.tar)|
|EAST|MobileNetV3|81.67%|79.83%|80.74%|[Download link](https://paddleocr.bj.bcebos.com/det_mv3_east.tar)|
|DB|ResNet50_vd|83.79%|80.65%|82.19%|[Download link](https://paddleocr.bj.bcebos.com/det_r50_vd_db.tar)|
|DB|MobileNetV3|75.92%|73.18%|74.53%|[Download link](https://paddleocr.bj.bcebos.com/det_mv3_db.tar)|
|SAST|ResNet50_vd|92.18%|82.96%|87.33%|[Download link](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_icdar2015.tar)|
On Total-Text dataset, the text detection result is as follows:
|Model|Backbone|precision|recall|Hmean|Download link|
|-|-|-|-|-|-|
|SAST|ResNet50_vd|88.74%|79.80%|84.03%|[Download link](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_total_text.tar)|
**Note：** Additional data, like icdar2013, icdar2017, COCO-Text, ArT, was added to the model training of SAST. Download English public dataset in organized format used by PaddleOCR from [Baidu Drive](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (download code: 2bpi).
For use of [LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/datasets_en.md#1-icdar2019-lsvt) street view dataset with a total of 3w training data，the related configuration and pre-trained models for text detection task are as follows:
|Model|Backbone|Configuration file|Pre-trained model|
|-|-|-|-|
|ultra-lightweight OCR model|MobileNetV3|det_mv3_db.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|
|General OCR model|ResNet50_vd|det_r50_vd_db.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|
* Note: For the training and evaluation of the above DB model, post-processing parameters box_thresh=0.6 and unclip_ratio=1.5 need to be set. If using different datasets and different models for training, these two parameters can be adjusted for better result.
For the training guide and use of PaddleOCR text detection algorithms, please refer to the document [Text detection model training/evaluation/prediction](./doc/doc_en/detection_en.md)
<a name="TEXTRECOGNITIONALGORITHM"></a>
## Text Recognition Algorithm
PaddleOCR open-source text recognition algorithms list:
- [x] CRNN([paper](https://arxiv.org/abs/1507.05717))
- [x] Rosetta([paper](https://arxiv.org/abs/1910.05085))
- [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))
- [x] RARE([paper](https://arxiv.org/abs/1603.03915v1))
- [x] SRN([paper](https://arxiv.org/abs/2003.12294))(Baidu Self-Research)
Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation result of these above text recognition (using MJSynth and SynthText for training, evaluate on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE) is as follow:
|Model|Backbone|Avg Accuracy|Module combination|Download link|
|-|-|-|-|-|
|Rosetta|Resnet34_vd|80.24%|rec_r34_vd_none_none_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_none_ctc.tar)|
|Rosetta|MobileNetV3|78.16%|rec_mv3_none_none_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_mv3_none_none_ctc.tar)|
|CRNN|Resnet34_vd|82.20%|rec_r34_vd_none_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_bilstm_ctc.tar)|
|CRNN|MobileNetV3|79.37%|rec_mv3_none_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_mv3_none_bilstm_ctc.tar)|
|STAR-Net|Resnet34_vd|83.93%|rec_r34_vd_tps_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_ctc.tar)|
|STAR-Net|MobileNetV3|81.56%|rec_mv3_tps_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_ctc.tar)|
|RARE|Resnet34_vd|84.90%|rec_r34_vd_tps_bilstm_attn|[Download link](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_attn.tar)|
|RARE|MobileNetV3|83.32%|rec_mv3_tps_bilstm_attn|[Download link](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_attn.tar)|
|SRN|Resnet50_vd_fpn|88.33%|rec_r50fpn_vd_none_srn|[Download link](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar)|
**Note：** SRN model uses data expansion method to expand the two training sets mentioned above, and the expanded data can be downloaded from [Baidu Drive](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA) (download code: y3ry).
The average accuracy of the two-stage training in the original paper is 89.74%, and that of one stage training in paddleocr is 88.33%. Both pre-trained weights can be downloaded [here](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar).
We use [LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/datasets_en.md#1-icdar2019-lsvt) dataset and cropout 30w training data from original photos by using position groundtruth and make some calibration needed. In addition, based on the LSVT corpus, 500w synthetic data is generated to train the model. The related configuration and pre-trained models are as follows:
|Model|Backbone|Configuration file|Pre-trained model|
|-|-|-|-|
|ultra-lightweight OCR model|MobileNetV3|rec_chinese_lite_train.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar) & [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance.tar)|
|General OCR model|Resnet34_vd|rec_chinese_common_train.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance_infer.tar) & [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance.tar)|
Please refer to the document for training guide and use of PaddleOCR text recognition algorithms [Text recognition model training/evaluation/prediction](./doc/doc_en/recognition_en.md)
## Visualization
<a name="UCOCRVIS"></a>
### 1.Ultra-lightweight Chinese/English OCR Visualization [more](./doc/doc_en/visualization_en.md)
<div align="center">
<img src="doc/imgs_results/1.jpg" width="800">
</div>
<a name="GeOCRVIS"></a>
### 2. General Chinese/English OCR Visualization [more](./doc/doc_en/visualization_en.md)
<div align="center">
<img src="doc/imgs_results/chinese_db_crnn_server/11.jpg" width="800">
</div>
<a name="SpaceOCRVIS"></a>
### 3.Chinese/English OCR Visualization (Space_support) [more](./doc/doc_en/visualization_en.md)
<div align="center">
<img src="doc/imgs_results/chinese_db_crnn_server/en_paper.jpg" width="800">
</div>
<a name="FAQ"></a>
## FAQ
1. Error when using attention-based recognition model: KeyError: 'predict'
The inference of recognition model based on attention loss is still being debugged. For Chinese text recognition, it is recommended to choose the recognition model based on CTC loss first. In practice, it is also found that the recognition model based on attention loss is not as effective as the one based on CTC loss.
2. About inference speed
When there are a lot of texts in the picture, the prediction time will increase. You can use `--rec_batch_num` to set a smaller prediction batch size. The default value is 30, which can be changed to 10 or other values.
3. Service deployment and mobile deployment
It is expected that the service deployment based on Serving and the mobile deployment based on Paddle Lite will be released successively in mid-to-late June. Stay tuned for more updates.
4. Release time of self-developed algorithm
Baidu Self-developed algorithms such as SAST, SRN and end2end PSL will be released in June or July. Please be patient.
[more](./doc/doc_en/FAQ_en.md)
<a name="Community"></a>
## Community
Scan the QR code below with your wechat and completing the questionnaire, you can access to offical technical exchange group.
<div align="center">
<img src="./doc/joinus.PNG" width = "200" height = "200" />
</div>
<a name="LICENSE"></a>
## License
This project is released under <a href="https://github.com/PaddlePaddle/PaddleOCR/blob/master/LICENSE">Apache 2.0 license</a>
<a name="CONTRIBUTION"></a>
## Contribution
We welcome all the contributions to PaddleOCR and appreciate for your feedback very much.
- Many thanks to [Khanh Tran](https://github.com/xxxpsyduck) and [Karl Horky](https://github.com/karlhorky) for contributing and revising the English documentation.
- Many thanks to [zhangxin](https://github.com/ZhangXinNan) for contributing the new visualize function、add .gitgnore and discard set PYTHONPATH manually.
- Many thanks to [lyl120117](https://github.com/lyl120117) for contributing the code for printing the network structure.
- Thanks [xiangyubo](https://github.com/xiangyubo) for contributing the handwritten Chinese OCR datasets.
- Thanks [authorfu](https://github.com/authorfu) for contributing Android demo and [xiadeye](https://github.com/xiadeye) contributing iOS demo, respectively.
- Thanks [BeyondYourself](https://github.com/BeyondYourself) for contributing many great suggestions and simplifying part of the code style.
- Thanks [tangmq](https://gitee.com/tangmq) for contributing Dockerized deployment services to PaddleOCR and supporting the rapid release of callable Restful API services.
__init__.py 0 → 100644
浏览文件 @ 5fc9fff9
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
__all__ = ['PaddleOCR', 'draw_ocr']
from .paddleocr import PaddleOCR
from .tools.infer.utility import draw_ocr
deploy/cpp_infer/include/config.h
浏览文件 @ 5fc9fff9
......@@ -41,6 +41,8 @@ public:
this->use_mkldnn = bool(stoi(config_map_["use_mkldnn"]));
this->use_zero_copy_run = bool(stoi(config_map_["use_zero_copy_run"]));
this->max_side_len = stoi(config_map_["max_side_len"]);
this->det_db_thresh = stod(config_map_["det_db_thresh"]);
......@@ -68,6 +70,8 @@ public:
bool use_mkldnn = false;
bool use_zero_copy_run = false;
int max_side_len = 960;
double det_db_thresh = 0.3;
......
deploy/cpp_infer/include/ocr_det.h
浏览文件 @ 5fc9fff9
......@@ -39,8 +39,8 @@ public:
explicit DBDetector(const std::string &model_dir, const bool &use_gpu,
const int &gpu_id, const int &gpu_mem,
const int &cpu_math_library_num_threads,
const bool &use_mkldnn, const int &max_side_len,
const double &det_db_thresh,
const bool &use_mkldnn, const bool &use_zero_copy_run,
const int &max_side_len, const double &det_db_thresh,
const double &det_db_box_thresh,
const double &det_db_unclip_ratio,
const bool &visualize) {
......@@ -49,6 +49,7 @@ public:
this->gpu_mem_ = gpu_mem;
this->cpu_math_library_num_threads_ = cpu_math_library_num_threads;
this->use_mkldnn_ = use_mkldnn;
this->use_zero_copy_run_ = use_zero_copy_run;
this->max_side_len_ = max_side_len;
......@@ -75,6 +76,7 @@ private:
int gpu_mem_ = 4000;
int cpu_math_library_num_threads_ = 4;
bool use_mkldnn_ = false;
bool use_zero_copy_run_ = false;
int max_side_len_ = 960;
......
deploy/cpp_infer/include/ocr_rec.h
浏览文件 @ 5fc9fff9
......@@ -38,12 +38,14 @@ public:
explicit CRNNRecognizer(const std::string &model_dir, const bool &use_gpu,
const int &gpu_id, const int &gpu_mem,
const int &cpu_math_library_num_threads,
const bool &use_mkldnn, const string &label_path) {
const bool &use_mkldnn, const bool &use_zero_copy_run,
const string &label_path) {
this->use_gpu_ = use_gpu;
this->gpu_id_ = gpu_id;
this->gpu_mem_ = gpu_mem;
this->cpu_math_library_num_threads_ = cpu_math_library_num_threads;
this->use_mkldnn_ = use_mkldnn;
this->use_zero_copy_run_ = use_zero_copy_run;
this->label_list_ = Utility::ReadDict(label_path);
this->label_list_.push_back(" ");
......@@ -64,6 +66,7 @@ private:
int gpu_mem_ = 4000;
int cpu_math_library_num_threads_ = 4;
bool use_mkldnn_ = false;
bool use_zero_copy_run_ = false;
std::vector<std::string> label_list_;
......
deploy/cpp_infer/src/main.cpp
浏览文件 @ 5fc9fff9
......@@ -48,14 +48,15 @@ int main(int argc, char **argv) {
cv::Mat srcimg = cv::imread(img_path, cv::IMREAD_COLOR);
DBDetector det(config.det_model_dir, config.use_gpu, config.gpu_id,
config.gpu_mem, config.cpu_math_library_num_threads,
config.use_mkldnn, config.max_side_len, config.det_db_thresh,
config.det_db_box_thresh, config.det_db_unclip_ratio,
config.visualize);
DBDetector det(
config.det_model_dir, config.use_gpu, config.gpu_id, config.gpu_mem,
config.cpu_math_library_num_threads, config.use_mkldnn,
config.use_zero_copy_run, config.max_side_len, config.det_db_thresh,
config.det_db_box_thresh, config.det_db_unclip_ratio, config.visualize);
CRNNRecognizer rec(config.rec_model_dir, config.use_gpu, config.gpu_id,
config.gpu_mem, config.cpu_math_library_num_threads,
config.use_mkldnn, config.char_list_file);
config.use_mkldnn, config.use_zero_copy_run,
config.char_list_file);
auto start = std::chrono::system_clock::now();
std::vector<std::vector<std::vector<int>>> boxes;
......
deploy/cpp_infer/src/ocr_det.cpp
浏览文件 @ 5fc9fff9
......@@ -31,7 +31,8 @@ void DBDetector::LoadModel(const std::string &model_dir) {
}
// false for zero copy tensor
config.SwitchUseFeedFetchOps(false);
// true for commom tensor
config.SwitchUseFeedFetchOps(!this->use_zero_copy_run_);
// true for multiple input
config.SwitchSpecifyInputNames(true);
......@@ -59,12 +60,22 @@ void DBDetector::Run(cv::Mat &img,
std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
this->permute_op_.Run(&resize_img, input.data());
auto input_names = this->predictor_->GetInputNames();
auto input_t = this->predictor_->GetInputTensor(input_names[0]);
input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
input_t->copy_from_cpu(input.data());
this->predictor_->ZeroCopyRun();
// Inference.
if (this->use_zero_copy_run_) {
auto input_names = this->predictor_->GetInputNames();
auto input_t = this->predictor_->GetInputTensor(input_names[0]);
input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
input_t->copy_from_cpu(input.data());
this->predictor_->ZeroCopyRun();
} else {
paddle::PaddleTensor input_t;
input_t.shape = {1, 3, resize_img.rows, resize_img.cols};
input_t.data =
paddle::PaddleBuf(input.data(), input.size() * sizeof(float));
input_t.dtype = PaddleDType::FLOAT32;
std::vector<paddle::PaddleTensor> outputs;
this->predictor_->Run({input_t}, &outputs, 1);
}
std::vector<float> out_data;
auto output_names = this->predictor_->GetOutputNames();
......
deploy/cpp_infer/src/ocr_rec.cpp
浏览文件 @ 5fc9fff9
......@@ -39,18 +39,29 @@ void CRNNRecognizer::Run(std::vector<std::vector<std::vector<int>>> boxes,
this->permute_op_.Run(&resize_img, input.data());
auto input_names = this->predictor_->GetInputNames();
auto input_t = this->predictor_->GetInputTensor(input_names[0]);
input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
input_t->copy_from_cpu(input.data());
this->predictor_->ZeroCopyRun();
// Inference.
if (this->use_zero_copy_run_) {
auto input_names = this->predictor_->GetInputNames();
auto input_t = this->predictor_->GetInputTensor(input_names[0]);
input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
input_t->copy_from_cpu(input.data());
this->predictor_->ZeroCopyRun();
} else {
paddle::PaddleTensor input_t;
input_t.shape = {1, 3, resize_img.rows, resize_img.cols};
input_t.data =
paddle::PaddleBuf(input.data(), input.size() * sizeof(float));
input_t.dtype = PaddleDType::FLOAT32;
std::vector<paddle::PaddleTensor> outputs;
this->predictor_->Run({input_t}, &outputs, 1);
}
std::vector<int64_t> rec_idx;
auto output_names = this->predictor_->GetOutputNames();
auto output_t = this->predictor_->GetOutputTensor(output_names[0]);
auto rec_idx_lod = output_t->lod();
auto shape_out = output_t->shape();
int out_num = std::accumulate(shape_out.begin(), shape_out.end(), 1,
std::multiplies<int>());
......@@ -120,7 +131,8 @@ void CRNNRecognizer::LoadModel(const std::string &model_dir) {
}
// false for zero copy tensor
config.SwitchUseFeedFetchOps(false);
// true for commom tensor
config.SwitchUseFeedFetchOps(!this->use_zero_copy_run_);
// true for multiple input
config.SwitchSpecifyInputNames(true);
......
deploy/cpp_infer/tools/config.txt
浏览文件 @ 5fc9fff9
......@@ -4,6 +4,7 @@ gpu_id 0
gpu_mem 4000
cpu_math_library_num_threads 10
use_mkldnn 0
use_zero_copy_run 1
# det config
max_side_len 960
......
deploy/hubserving/ocr_det/params.py
浏览文件 @ 5fc9fff9
......@@ -36,4 +36,6 @@ def read_params():
# cfg.rec_char_dict_path = "./ppocr/utils/ppocr_keys_v1.txt"
# cfg.use_space_char = True
return cfg
\ No newline at end of file
cfg.use_zero_copy_run = False
return cfg
deploy/hubserving/ocr_rec/params.py
浏览文件 @ 5fc9fff9
......@@ -38,4 +38,6 @@ def read_params():
cfg.rec_char_dict_path = "./ppocr/utils/ppocr_keys_v1.txt"
cfg.use_space_char = True
return cfg
\ No newline at end of file
cfg.use_zero_copy_run = False
return cfg
deploy/hubserving/ocr_system/params.py
浏览文件 @ 5fc9fff9
......@@ -38,4 +38,6 @@ def read_params():
cfg.rec_char_dict_path = "./ppocr/utils/ppocr_keys_v1.txt"
cfg.use_space_char = True
return cfg
\ No newline at end of file
cfg.use_zero_copy_run = False
return cfg
doc/doc_ch/FAQ.md
浏览文件 @ 5fc9fff9
此差异已折叠。
doc/doc_ch/config.md
浏览文件 @ 5fc9fff9
......@@ -63,8 +63,9 @@
| beta1 | 设置一阶矩估计的指数衰减率 | 0.9 | \ |
| beta2 | 设置二阶矩估计的指数衰减率 | 0.999 | \ |
| decay | 是否使用decay | \ | \ |
| function(decay) | 设置decay方式 | - | 目前支持cosine_decay与piecewise_decay |
| step_each_epoch | 每个epoch包含多少次迭代, cosine_decay时有效 | 20 | 计算方式：total_image_num / (batch_size_per_card * card_size) |
| total_epoch | 总共迭代多少个epoch, cosine_decay时有效 | 1000 | 与Global.epoch_num 一致 |
| function(decay) | 设置decay方式 | - | 目前支持cosine_decay, cosine_decay_warmup与piecewise_decay |
| step_each_epoch | 每个epoch包含多少次迭代, cosine_decay/cosine_decay_warmup时有效 | 20 | 计算方式：total_image_num / (batch_size_per_card * card_size) |
| total_epoch | 总共迭代多少个epoch, cosine_decay/cosine_decay_warmup时有效 | 1000 | 与Global.epoch_num 一致 |
| warmup_minibatch | 线性warmup的迭代次数, cosine_decay_warmup时有效 | 1000 | \ |
| boundaries | 学习率下降时的迭代次数间隔, piecewise_decay时有效 | - | 参数为列表形式 |
| decay_rate | 学习率衰减系数, piecewise_decay时有效 | - | \ |
doc/doc_ch/quickstart.md
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......@@ -5,6 +5,8 @@
请先参考[快速安装](./installation.md)配置PaddleOCR运行环境。
*注意：也可以通过 whl 包安装使用PaddleOCR，具体参考[Paddleocr Package使用说明](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/whl.md)。*
## 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|
......
doc/doc_ch/serving.md
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......@@ -61,6 +61,14 @@ hub install deploy\hubserving\ocr_rec\
hub install deploy\hubserving\ocr_system\
```
#### 安装模型
安装服务模块前，需要将训练好的模型放到对应的文件夹内。默认使用的是：
./inference/ch_det_mv3_db/
和
./inference/ch_rec_mv3_crnn/
这两个模型可以在https://github.com/PaddlePaddle/PaddleOCR 下载
可以在./deploy/hubserving/ocr_system/params.py 里面修改成自己的模型
### 3. 启动服务
#### 方式1. 命令行命令启动（仅支持CPU）
**启动命令：**
......
doc/doc_ch/update.md
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# 更新
- 2020.8.24 支持通过whl包安装使用PaddleOCR，具体参考[Paddleocr Package使用说明](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/whl.md)
- 2020.8.21 更新8月18日B站直播课回放和PPT，课节2，易学易用的OCR工具大礼包，[获取地址](https://aistudio.baidu.com/aistudio/education/group/info/1519)
- 2020.8.16 开源文本检测算法[SAST](https://arxiv.org/abs/1908.05498)和文本识别算法[SRN](https://arxiv.org/abs/2003.12294)
- 2020.7.23 发布7月21日B站直播课回放和PPT，PaddleOCR开源大礼包全面解读，[获取地址](https://aistudio.baidu.com/aistudio/course/introduce/1519)
- 2020.7.23 发布7月21日B站直播课回放和PPT，课节1，PaddleOCR开源大礼包全面解读，[获取地址](https://aistudio.baidu.com/aistudio/course/introduce/1519)
- 2020.7.15 添加基于EasyEdge和Paddle-Lite的移动端DEMO，支持iOS和Android系统
- 2020.7.15 完善预测部署，添加基于C++预测引擎推理、服务化部署和端侧部署方案，以及超轻量级中文OCR模型预测耗时Benchmark
- 2020.7.15 整理OCR相关数据集、常用数据标注以及合成工具
......
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# paddleocr package使用说明
## 快速上手
### 安装whl包
pip安装
```bash
pip install paddleocr
```
本地构建并安装
```bash
python setup.py bdist_wheel
pip install dist/paddleocr-0.0.3-py3-none-any.whl
```
### 1. 代码使用
* 检测+识别全流程
```python
from paddleocr import PaddleOCR, draw_ocr
ocr = PaddleOCR() # need to run only once to download and load model into memory
img_path = 'PaddleOCR/doc/imgs/11.jpg'
result = ocr.ocr(img_path)
for line in result:
print(line)
# 显示结果
from PIL import Image
image = Image.open(img_path).convert('RGB')
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
scores = [line[1][1] for line in result]
im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')
```
结果是一个list，每个item包含了文本框，文字和识别置信度
```bash
[[[24.0, 36.0], [304.0, 34.0], [304.0, 72.0], [24.0, 74.0]], ['纯臻营养护发素', 0.964739]]
[[[24.0, 80.0], [172.0, 80.0], [172.0, 104.0], [24.0, 104.0]], ['产品信息/参数', 0.98069626]]
[[[24.0, 109.0], [333.0, 109.0], [333.0, 136.0], [24.0, 136.0]], ['（45元/每公斤，100公斤起订）', 0.9676722]]
......
```
结果可视化
<div align="center">
<img src="../imgs_results/whl/11_det_rec.jpg" width="800">
</div>
* 单独执行检测
```python
from paddleocr import PaddleOCR, draw_ocr
ocr = PaddleOCR() # need to run only once to download and load model into memory
img_path = 'PaddleOCR/doc/imgs/11.jpg'
result = ocr.ocr(img_path,rec=False)
for line in result:
print(line)
# 显示结果
from PIL import Image
image = Image.open(img_path).convert('RGB')
im_show = draw_ocr(image, result, txts=None, scores=None, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')
```
结果是一个list，每个item只包含文本框
```bash
[[26.0, 457.0], [137.0, 457.0], [137.0, 477.0], [26.0, 477.0]]
[[25.0, 425.0], [372.0, 425.0], [372.0, 448.0], [25.0, 448.0]]
[[128.0, 397.0], [273.0, 397.0], [273.0, 414.0], [128.0, 414.0]]
......
```
结果可视化
<div align="center">
<img src="../imgs_results/whl/11_det.jpg" width="800">
</div>
* 单独执行识别
```python
from paddleocr import PaddleOCR
ocr = PaddleOCR() # need to run only once to download and load model into memory
img_path = 'PaddleOCR/doc/imgs_words/ch/word_1.jpg'
result = ocr.ocr(img_path,det=False)
for line in result:
print(line)
```
结果是一个list，每个item只包含识别结果和识别置信度
```bash
['韩国小馆', 0.9907421]
```
### 通过命令行使用
查看帮助信息
```bash
paddleocr -h
```
* 检测+识别全流程
```bash
paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg
```
结果是一个list，每个item包含了文本框，文字和识别置信度
```bash
[[[24.0, 36.0], [304.0, 34.0], [304.0, 72.0], [24.0, 74.0]], ['纯臻营养护发素', 0.964739]]
[[[24.0, 80.0], [172.0, 80.0], [172.0, 104.0], [24.0, 104.0]], ['产品信息/参数', 0.98069626]]
[[[24.0, 109.0], [333.0, 109.0], [333.0, 136.0], [24.0, 136.0]], ['（45元/每公斤，100公斤起订）', 0.9676722]]
......
```
* 单独执行检测
```bash
paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --rec false
```
结果是一个list，每个item只包含文本框
```bash
[[26.0, 457.0], [137.0, 457.0], [137.0, 477.0], [26.0, 477.0]]
[[25.0, 425.0], [372.0, 425.0], [372.0, 448.0], [25.0, 448.0]]
[[128.0, 397.0], [273.0, 397.0], [273.0, 414.0], [128.0, 414.0]]
......
```
* 单独执行识别
```bash
paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --det false
```
结果是一个list，每个item只包含识别结果和识别置信度
```bash
['韩国小馆', 0.9907421]
```
## 自定义模型
当内置模型无法满足需求时，需要使用到自己训练的模型。
首先，参照[inference.md](./inference.md) 第一节转换将检测和识别模型转换为inference模型，然后按照如下方式使用
### 代码使用
```python
from paddleocr import PaddleOCR, draw_ocr
# 检测模型和识别模型路径下必须含有model和params文件
ocr = PaddleOCR(det_model_dir='{your_det_model_dir}',rec_model_dir='{your_rec_model_dir}')
img_path = 'PaddleOCR/doc/imgs/11.jpg'
result = ocr.ocr(img_path)
for line in result:
print(line)
# 显示结果
from PIL import Image
image = Image.open(img_path).convert('RGB')
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
scores = [line[1][1] for line in result]
im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')
```
### 通过命令行使用
```bash
paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_dir} --rec_model_dir {your_rec_model_dir}
```
## 参数说明
| 字段 | 说明 | 默认值 |
|-------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------|
| use_gpu | 是否使用GPU | TRUE |
| gpu_mem | 初始化占用的GPU内存大小 | 8000M |
| image_dir | 通过命令行调用时执行预测的图片或文件夹路径 | |
| det_algorithm | 使用的检测算法类型 | DB |
| det_model_dir | 检测模型所在文件夹。传参方式有两种，1. None: 自动下载内置模型到 `~/.paddleocr/det`；2.自己转换好的inference模型路径，模型路径下必须包含model和params文件 | None |
| det_max_side_len | 检测算法前向时图片长边的最大尺寸，当长边超出这个值时会将长边resize到这个大小，短边等比例缩放 | 960 |
| det_db_thresh | DB模型输出预测图的二值化阈值 | 0.3 |
| det_db_box_thresh | DB模型输出框的阈值，低于此值的预测框会被丢弃 | 0.5 |
| det_db_unclip_ratio | DB模型输出框扩大的比例 | 2 |
| det_east_score_thresh | EAST模型输出预测图的二值化阈值 | 0.8 |
| det_east_cover_thresh | EAST模型输出框的阈值，低于此值的预测框会被丢弃 | 0.1 |
| det_east_nms_thresh | EAST模型输出框NMS的阈值 | 0.2 |
| rec_algorithm | 使用的识别算法类型 | CRNN |
| rec_model_dir | 识别模型所在文件夹。传承那方式有两种，1. None: 自动下载内置模型到 `~/.paddleocr/rec`；2.自己转换好的inference模型路径，模型路径下必须包含model和params文件 | None |
| rec_image_shape | 识别算法的输入图片尺寸 | "3,32,320" |
| rec_char_type | 识别算法的字符类型，中文(ch)或英文(en) | ch |
| rec_batch_num | 进行识别时，同时前向的图片数 | 30 |
| max_text_length | 识别算法能识别的最大文字长度 | 25 |
| rec_char_dict_path | 识别模型字典路径，当rec_model_dir使用方式2传参时需要修改为自己的字典路径 | ./ppocr/utils/ppocr_keys_v1.txt |
| use_space_char | 是否识别空格 | TRUE |
| enable_mkldnn | 是否启用mkldnn | FALSE |
| det | 前向时使用启动检测 | TRUE |
| rec | 前向时是否启动识别 | TRUE |
doc/doc_en/config_en.md
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......@@ -60,8 +60,9 @@ Take `rec_icdar15_train.yml` as an example:
| beta1 | Set the exponential decay rate for the 1st moment estimates | 0.9 | \ |
| beta2 | Set the exponential decay rate for the 2nd moment estimates | 0.999 | \ |
| decay | Whether to use decay | \ | \ |
| function(decay) | Set the decay function | cosine_decay | Support cosine_decay and piecewise_decay |
| step_each_epoch | The number of steps in an epoch. Used in cosine_decay | 20 | Calculation ：total_image_num / (batch_size_per_card * card_size) |
| total_epoch | The number of epochs. Used in cosine_decay | 1000 | Consistent with Global.epoch_num |
| function(decay) | Set the decay function | cosine_decay | Support cosine_decay, cosine_decay_warmup and piecewise_decay |
| step_each_epoch | The number of steps in an epoch. Used in cosine_decay/cosine_decay_warmup | 20 | Calculation: total_image_num / (batch_size_per_card * card_size) |
| total_epoch | The number of epochs. Used in cosine_decay/cosine_decay_warmup | 1000 | Consistent with Global.epoch_num |
| warmup_minibatch | Number of steps for linear warmup. Used in cosine_decay_warmup | 1000 | \ |
| boundaries | The step intervals to reduce learning rate. Used in piecewise_decay | - | The format is list |
| decay_rate | Learning rate decay rate. Used in piecewise_decay | - | \ |
doc/doc_en/quickstart_en.md
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......@@ -5,6 +5,7 @@
Please refer to [quick installation](./installation_en.md) to configure the PaddleOCR operating environment.
*Note: Support the use of PaddleOCR through whl package installation，pelease refer [PaddleOCR Package](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/whl_en.md)。*
## 2.inference models
......
doc/doc_en/update_en.md
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# RECENT UPDATES
- 2020.8.24 Support the use of PaddleOCR through whl package installation，pelease refer [PaddleOCR Package](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/whl_en.md)
- 2020.8.16 Release text detection algorithm [SAST](https://arxiv.org/abs/1908.05498) and text recognition algorithm [SRN](https://arxiv.org/abs/2003.12294)
- 2020.7.23, Release the playback and PPT of live class on BiliBili station, PaddleOCR Introduction, [address](https://aistudio.baidu.com/aistudio/course/introduce/1519)
- 2020.7.15, Add mobile App demo , support both iOS and Android ( based on easyedge and Paddle Lite)
......
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# paddleocr package
## Get started quickly
### install package
install by pypi
```bash
pip install paddleocr
```
build own whl package and install
```bash
python setup.py bdist_wheel
pip install dist/paddleocr-0.0.3-py3-none-any.whl
```
### 1. Use by code
* detection and recognition
```python
from paddleocr import PaddleOCR,draw_ocr
ocr = PaddleOCR() # need to run only once to download and load model into memory
img_path = 'PaddleOCR/doc/imgs_en/img_12.jpg'
result = ocr.ocr(img_path)
for line in result:
print(line)
# draw result
from PIL import Image
image = Image.open(img_path).convert('RGB')
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
scores = [line[1][1] for line in result]
im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')
```
Output will be a list, each item contains bounding box, text and recognition confidence
```bash
[[[442.0, 173.0], [1169.0, 173.0], [1169.0, 225.0], [442.0, 225.0]], ['ACKNOWLEDGEMENTS', 0.99283075]]
[[[393.0, 340.0], [1207.0, 342.0], [1207.0, 389.0], [393.0, 387.0]], ['We would like to thank all the designers and', 0.9357758]]
[[[399.0, 398.0], [1204.0, 398.0], [1204.0, 433.0], [399.0, 433.0]], ['contributors whohave been involved in the', 0.9592447]]
......
```
Visualization of results
<div align="center">
<img src="../imgs_results/whl/12_det_rec.jpg" width="800">
</div>
* only detection
```python
from paddleocr import PaddleOCR,draw_ocr
ocr = PaddleOCR() # need to run only once to download and load model into memory
img_path = 'PaddleOCR/doc/imgs_en/img_12.jpg'
result = ocr.ocr(img_path,rec=False)
for line in result:
print(line)
# draw result
from PIL import Image
image = Image.open(img_path).convert('RGB')
im_show = draw_ocr(image, result, txts=None, scores=None, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')
```
Output will be a list, each item only contains bounding box
```bash
[[756.0, 812.0], [805.0, 812.0], [805.0, 830.0], [756.0, 830.0]]
[[820.0, 803.0], [1085.0, 801.0], [1085.0, 836.0], [820.0, 838.0]]
[[393.0, 801.0], [715.0, 805.0], [715.0, 839.0], [393.0, 836.0]]
......
```
Visualization of results
<div align="center">
<img src="../imgs_results/whl/12_det.jpg" width="800">
</div>
* only recognition
```python
from paddleocr import PaddleOCR
ocr = PaddleOCR() # need to run only once to load model into memory
img_path = 'PaddleOCR/doc/imgs_words_en/word_10.png'
result = ocr.ocr(img_path,det=False)
for line in result:
print(line)
```
Output will be a list, each item contains text and recognition confidence
```bash
['PAIN', 0.990372]
```
### Use by command line
show help information
```bash
paddleocr -h
```
* detection and recognition
```bash
paddleocr --image_dir PaddleOCR/doc/imgs_en/img_12.jpg
```
Output will be a list, each item contains bounding box, text and recognition confidence
```bash
[[[442.0, 173.0], [1169.0, 173.0], [1169.0, 225.0], [442.0, 225.0]], ['ACKNOWLEDGEMENTS', 0.99283075]]
[[[393.0, 340.0], [1207.0, 342.0], [1207.0, 389.0], [393.0, 387.0]], ['We would like to thank all the designers and', 0.9357758]]
[[[399.0, 398.0], [1204.0, 398.0], [1204.0, 433.0], [399.0, 433.0]], ['contributors whohave been involved in the', 0.9592447]]
......
```
* only detection
```bash
paddleocr --image_dir PaddleOCR/doc/imgs_en/img_12.jpg --rec false
```
Output will be a list, each item only contains bounding box
```bash
[[756.0, 812.0], [805.0, 812.0], [805.0, 830.0], [756.0, 830.0]]
[[820.0, 803.0], [1085.0, 801.0], [1085.0, 836.0], [820.0, 838.0]]
[[393.0, 801.0], [715.0, 805.0], [715.0, 839.0], [393.0, 836.0]]
......
```
* only recognition
```bash
paddleocr --image_dir PaddleOCR/doc/imgs_words_en/word_10.png --det false
```
Output will be a list, each item contains text and recognition confidence
```bash
['PAIN', 0.990372]
```
## Use custom model
When the built-in model cannot meet the needs, you need to use your own trained model.
First, refer to the first section of [inference_en.md](./inference_en.md) to convert your det and rec model to inference model, and then use it as follows
### 1. Use by code
```python
from paddleocr import PaddleOCR,draw_ocr
# The path of detection and recognition model must contain model and params files
ocr = PaddleOCR(det_model_dir='{your_det_model_dir}',rec_model_dir='{your_rec_model_dir}å')
img_path = 'PaddleOCR/doc/imgs_en/img_12.jpg'
result = ocr.ocr(img_path)
for line in result:
print(line)
# draw result
from PIL import Image
image = Image.open(img_path).convert('RGB')
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
scores = [line[1][1] for line in result]
im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')
```
### Use by command line
```bash
paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_dir} --rec_model_dir {your_rec_model_dir}
```
## Parameter Description
| Parameter | Description | Default value |
|-------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------|
| use_gpu | use GPU or not | TRUE |
| gpu_mem | GPU memory size used for initialization | 8000M |
| image_dir | The images path or folder path for predicting when used by the command line | |
| det_algorithm | Type of detection algorithm selected | DB |
| det_model_dir | the text detection inference model folder. There are two ways to transfer parameters, 1. None: Automatically download the built-in model to `~/.paddleocr/det`; 2. The path of the inference model converted by yourself, the model and params files must be included in the model path | None |
| det_max_side_len | The maximum size of the long side of the image. When the long side exceeds this value, the long side will be resized to this size, and the short side will be scaled proportionally | 960 |
| det_db_thresh | Binarization threshold value of DB output map | 0.3 |
| det_db_box_thresh | The threshold value of the DB output box. Boxes score lower than this value will be discarded | 0.5 |
| det_db_unclip_ratio | The expanded ratio of DB output box | 2 |
| det_east_score_thresh | Binarization threshold value of EAST output map | 0.8 |
| det_east_cover_thresh | The threshold value of the EAST output box. Boxes score lower than this value will be discarded | 0.1 |
| det_east_nms_thresh | The NMS threshold value of EAST model output box | 0.2 |
| rec_algorithm | Type of recognition algorithm selected | CRNN |
| rec_model_dir | the text recognition inference model folder. There are two ways to transfer parameters, 1. None: Automatically download the built-in model to `~/.paddleocr/rec`; 2. The path of the inference model converted by yourself, the model and params files must be included in the model path | None |
| rec_image_shape | image shape of recognition algorithm | "3,32,320" |
| rec_char_type | Character type of recognition algorithm, Chinese (ch) or English (en) | ch |
| rec_batch_num | When performing recognition, the batchsize of forward images | 30 |
| max_text_length | The maximum text length that the recognition algorithm can recognize | 25 |
| rec_char_dict_path | the alphabet path which needs to be modified to your own path when `rec_model_Name` use mode 2 | ./ppocr/utils/ppocr_keys_v1.txt |
| use_space_char | Whether to recognize spaces | TRUE |
| enable_mkldnn | Whether to enable mkldnn | FALSE |
| det | Enable detction when `ppocr.ocr` func exec | TRUE |
| rec | Enable detction when `ppocr.ocr` func exec | TRUE |
docker/hubserving/README.md 0 → 100644
浏览文件 @ 5fc9fff9
English | [简体中文](README_cn.md)
## Introduction
Many user hopes package the PaddleOCR service into an docker image, so that it can be quickly released and used in the docker or k8s environment.
This page provide some standardized code to achieve this goal. You can quickly publish the PaddleOCR project into a callable Restful API service through the following steps. (At present, the deployment based on the HubServing mode is implemented first, and author plans to increase the deployment of the PaddleServing mode in the futrue)
## 1. Prerequisites
You need to install the following basic components first：
a. Docker
b. Graphics driver and CUDA 10.0+（GPU）
c. NVIDIA Container Toolkit（GPU，Docker 19.03+ can skip this）
d. cuDNN 7.6+（GPU）
## 2. Build Image
a. Download PaddleOCR sourcecode
```
git clone https://github.com/PaddlePaddle/PaddleOCR.git
```
b. Goto Dockerfile directory（ps：Need to distinguish between cpu and gpu version, the following takes cpu as an example, gpu version needs to replace the keyword）
```
cd docker/cpu
```
c. Build image
```
docker build -t paddleocr:cpu .
```
## 3. Start container
a. CPU version
```
sudo docker run -dp 8866:8866 --name paddle_ocr paddleocr:cpu
```
b. GPU version (base on NVIDIA Container Toolkit)
```
sudo nvidia-docker run -dp 8866:8866 --name paddle_ocr paddleocr:gpu
```
c. GPU version (Docker 19.03++)
```
sudo docker run -dp 8866:8866 --gpus all --name paddle_ocr paddleocr:gpu
```
d. Check service status（If you can see the following statement then it means completed：Successfully installed ocr_system && Running on http://0.0.0.0:8866/）
```
docker logs -f paddle_ocr
```
## 4. Test
a. Calculate the Base64 encoding of the picture to be recognized (if you just test, you can use a free online tool, like：https://freeonlinetools24.com/base64-image/）
b. Post a service request（sample request in sample_request.txt）
```
curl -H "Content-Type:application/json" -X POST --data "{\"images\": [\"Input image Base64 encode(need to delete the code 'data:image/jpg;base64,'）\"]}" http://localhost:8866/predict/ocr_system
```
c. Get resposne（If the call is successful, the following result will be returned）
```
{"msg":"","results":[[{"confidence":0.8403433561325073,"text":"约定","text_region":[[345,377],[641,390],[634,540],[339,528]]},{"confidence":0.8131805658340454,"text":"最终相遇","text_region":[[356,532],[624,530],[624,596],[356,598]]}]],"status":"0"}
```
docker/hubserving/readme.md → docker/hubserving/README_cn.md
浏览文件 @ 5fc9fff9
# Docker化部署服务
[English](README.md) | 简体中文
## Docker化部署服务
在日常项目应用中，相信大家一般都会希望能通过Docker技术，把PaddleOCR服务打包成一个镜像，以便在Docker或k8s环境里，快速发布上线使用。
本文将提供一些标准化的代码来实现这样的目标。大家通过如下步骤可以把PaddleOCR项目快速发布成可调用的Restful API服务。（目前暂时先实现了基于HubServing模式的部署，后续作者计划增加PaddleServing模式的部署）
......
paddleocr.py 0 → 100644
浏览文件 @ 5fc9fff9
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
__dir__ = os.path.dirname(__file__)
sys.path.append(os.path.join(__dir__, ''))
import cv2
import numpy as np
from pathlib import Path
import tarfile
import requests
from tqdm import tqdm
from tools.infer import predict_system
from ppocr.utils.utility import initial_logger
logger = initial_logger()
from ppocr.utils.utility import check_and_read_gif, get_image_file_list
__all__ = ['PaddleOCR']
model_params = {
'det': 'https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar',
'rec':
'https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar',
}
SUPPORT_DET_MODEL = ['DB']
SUPPORT_REC_MODEL = ['CRNN']
BASE_DIR = os.path.expanduser("~/.paddleocr/")
def download_with_progressbar(url, save_path):
response = requests.get(url, stream=True)
total_size_in_bytes = int(response.headers.get('content-length', 0))
block_size = 1024 # 1 Kibibyte
progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True)
with open(save_path, 'wb') as file:
for data in response.iter_content(block_size):
progress_bar.update(len(data))
file.write(data)
progress_bar.close()
if total_size_in_bytes != 0 and progress_bar.n != total_size_in_bytes:
logger.error("ERROR, something went wrong")
sys.exit(0)
def maybe_download(model_storage_directory, url):
# using custom model
if not os.path.exists(os.path.join(
model_storage_directory, 'model')) or not os.path.exists(
os.path.join(model_storage_directory, 'params')):
tmp_path = os.path.join(model_storage_directory, url.split('/')[-1])
print('download {} to {}'.format(url, tmp_path))
os.makedirs(model_storage_directory, exist_ok=True)
download_with_progressbar(url, tmp_path)
with tarfile.open(tmp_path, 'r') as tarObj:
for member in tarObj.getmembers():
if "model" in member.name:
filename = 'model'
elif "params" in member.name:
filename = 'params'
else:
continue
file = tarObj.extractfile(member)
with open(
os.path.join(model_storage_directory, filename),
'wb') as f:
f.write(file.read())
os.remove(tmp_path)
def parse_args():
import argparse
def str2bool(v):
return v.lower() in ("true", "t", "1")
parser = argparse.ArgumentParser()
# params for prediction engine
parser.add_argument("--use_gpu", type=str2bool, default=True)
parser.add_argument("--ir_optim", type=str2bool, default=True)
parser.add_argument("--use_tensorrt", type=str2bool, default=False)
parser.add_argument("--gpu_mem", type=int, default=8000)
# params for text detector
parser.add_argument("--image_dir", type=str)
parser.add_argument("--det_algorithm", type=str, default='DB')
parser.add_argument("--det_model_dir", type=str, default=None)
parser.add_argument("--det_max_side_len", type=float, default=960)
# DB parmas
parser.add_argument("--det_db_thresh", type=float, default=0.3)
parser.add_argument("--det_db_box_thresh", type=float, default=0.5)
parser.add_argument("--det_db_unclip_ratio", type=float, default=2.0)
# EAST parmas
parser.add_argument("--det_east_score_thresh", type=float, default=0.8)
parser.add_argument("--det_east_cover_thresh", type=float, default=0.1)
parser.add_argument("--det_east_nms_thresh", type=float, default=0.2)
# params for text recognizer
parser.add_argument("--rec_algorithm", type=str, default='CRNN')
parser.add_argument("--rec_model_dir", type=str, default=None)
parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320")
parser.add_argument("--rec_char_type", type=str, default='ch')
parser.add_argument("--rec_batch_num", type=int, default=30)
parser.add_argument("--max_text_length", type=int, default=25)
parser.add_argument(
"--rec_char_dict_path",
type=str,
default="./ppocr/utils/ppocr_keys_v1.txt")
parser.add_argument("--use_space_char", type=bool, default=True)
parser.add_argument("--enable_mkldnn", type=bool, default=False)
parser.add_argument("--det", type=str2bool, default=True)
parser.add_argument("--rec", type=str2bool, default=True)
parser.add_argument("--use_zero_copy_run", type=bool, default=False)
return parser.parse_args()
class PaddleOCR(predict_system.TextSystem):
def __init__(self, **kwargs):
"""
paddleocr package
args:
**kwargs: other params show in paddleocr --help
"""
postprocess_params = parse_args()
postprocess_params.__dict__.update(**kwargs)
# init model dir
if postprocess_params.det_model_dir is None:
postprocess_params.det_model_dir = os.path.join(BASE_DIR, 'det')
if postprocess_params.rec_model_dir is None:
postprocess_params.rec_model_dir = os.path.join(BASE_DIR, 'rec')
print(postprocess_params)
# download model
maybe_download(postprocess_params.det_model_dir, model_params['det'])
maybe_download(postprocess_params.rec_model_dir, model_params['rec'])
if postprocess_params.det_algorithm not in SUPPORT_DET_MODEL:
logger.error('det_algorithm must in {}'.format(SUPPORT_DET_MODEL))
sys.exit(0)
if postprocess_params.rec_algorithm not in SUPPORT_REC_MODEL:
logger.error('rec_algorithm must in {}'.format(SUPPORT_REC_MODEL))
sys.exit(0)
postprocess_params.rec_char_dict_path = Path(
__file__).parent / postprocess_params.rec_char_dict_path
# init det_model and rec_model
super().__init__(postprocess_params)
def ocr(self, img, det=True, rec=True):
"""
ocr with paddleocr
args：
img: img for ocr, support ndarray, img_path and list or ndarray
det: use text detection or not, if false, only rec will be exec. default is True
rec: use text recognition or not, if false, only det will be exec. default is True
"""
assert isinstance(img, (np.ndarray, list, str))
if isinstance(img, str):
image_file = img
img, flag = check_and_read_gif(image_file)
if not flag:
img = cv2.imread(image_file)
if img is None:
logger.error("error in loading image:{}".format(image_file))
return None
if det and rec:
dt_boxes, rec_res = self.__call__(img)
return [[box.tolist(), res] for box, res in zip(dt_boxes, rec_res)]
elif det and not rec:
dt_boxes, elapse = self.text_detector(img)
if dt_boxes is None:
return None
return [box.tolist() for box in dt_boxes]
else:
if not isinstance(img, list):
img = [img]
rec_res, elapse = self.text_recognizer(img)
return rec_res
def main():
# for com
args = parse_args()
image_file_list = get_image_file_list(args.image_dir)
if len(image_file_list) == 0:
logger.error('no images find in {}'.format(args.image_dir))
return
ocr_engine = PaddleOCR()
for img_path in image_file_list:
print(img_path)
result = ocr_engine.ocr(img_path, det=args.det, rec=args.rec)
for line in result:
print(line)
\ No newline at end of file
ppocr/optimizer.py
浏览文件 @ 5fc9fff9
......@@ -14,14 +14,50 @@
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import paddle.fluid as fluid
from paddle.fluid.regularizer import L2Decay
from paddle.fluid.layers.learning_rate_scheduler import _decay_step_counter
import paddle.fluid.layers.ops as ops
from ppocr.utils.utility import initial_logger
logger = initial_logger()
def cosine_decay_with_warmup(learning_rate,
step_each_epoch,
epochs=500,
warmup_minibatch=1000):
"""Applies cosine decay to the learning rate.
lr = 0.05 * (math.cos(epoch * (math.pi / 120)) + 1)
decrease lr for every mini-batch and start with warmup.
"""
global_step = _decay_step_counter()
lr = fluid.layers.tensor.create_global_var(
shape=[1],
value=0.0,
dtype='float32',
persistable=True,
name="learning_rate")
warmup_minibatch = fluid.layers.fill_constant(
shape=[1],
dtype='float32',
value=float(warmup_minibatch),
force_cpu=True)
with fluid.layers.control_flow.Switch() as switch:
with switch.case(global_step < warmup_minibatch):
decayed_lr = learning_rate * (1.0 * global_step / warmup_minibatch)
fluid.layers.tensor.assign(input=decayed_lr, output=lr)
with switch.default():
decayed_lr = learning_rate * \
(ops.cos((global_step - warmup_minibatch) * (math.pi / (epochs * step_each_epoch))) + 1)/2
fluid.layers.tensor.assign(input=decayed_lr, output=lr)
return lr
def AdamDecay(params, parameter_list=None):
"""
define optimizer function
......@@ -36,7 +72,9 @@ def AdamDecay(params, parameter_list=None):
l2_decay = params.get("l2_decay", 0.0)
if 'decay' in params:
supported_decay_mode = ["cosine_decay", "piecewise_decay"]
supported_decay_mode = [
"cosine_decay", "cosine_decay_warmup", "piecewise_decay"
]
params = params['decay']
decay_mode = params['function']
assert decay_mode in supported_decay_mode, "Supported decay mode is {}, but got {}".format(
......@@ -49,6 +87,15 @@ def AdamDecay(params, parameter_list=None):
learning_rate=base_lr,
step_each_epoch=step_each_epoch,
epochs=total_epoch)
elif decay_mode == "cosine_decay_warmup":
step_each_epoch = params['step_each_epoch']
total_epoch = params['total_epoch']
warmup_minibatch = params.get("warmup_minibatch", 1000)
base_lr = cosine_decay_with_warmup(
learning_rate=base_lr,
step_each_epoch=step_each_epoch,
epochs=total_epoch,
warmup_minibatch=warmup_minibatch)
elif decay_mode == "piecewise_decay":
boundaries = params["boundaries"]
decay_rate = params["decay_rate"]
......@@ -104,5 +151,5 @@ def RMSProp(params, parameter_list=None):
optimizer = fluid.optimizer.RMSProp(
learning_rate=base_lr,
regularization=fluid.regularizer.L2Decay(regularization_coeff=l2_decay))
return optimizer
\ No newline at end of file
return optimizer
requirments.txt
浏览文件 @ 5fc9fff9
shapely
imgaug
pyclipper
lmdb
\ No newline at end of file
lmdb
tqdm
numpy
\ No newline at end of file
setup.py 0 → 100644
浏览文件 @ 5fc9fff9
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from setuptools import setup
from io import open
with open('requirments.txt', encoding="utf-8-sig") as f:
requirements = f.readlines()
requirements.append('tqdm')
def readme():
with open('doc/doc_en/whl_en.md', encoding="utf-8-sig") as f:
README = f.read()
return README
setup(
name='paddleocr',
packages=['paddleocr'],
package_dir={'paddleocr': ''},
include_package_data=True,
entry_points={"console_scripts": ["paddleocr= paddleocr.paddleocr:main"]},
version='0.0.3',
install_requires=requirements,
license='Apache License 2.0',
description='Awesome OCR toolkits based on PaddlePaddle （8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices',
long_description=readme(),
long_description_content_type='text/markdown',
url='https://github.com/PaddlePaddle/PaddleOCR',
download_url='https://github.com/PaddlePaddle/PaddleOCR.git',
keywords=[
'ocr textdetection textrecognition paddleocr crnn east star-net rosetta ocrlite db chineseocr chinesetextdetection chinesetextrecognition'
],
classifiers=[
'Intended Audience :: Developers', 'Operating System :: OS Independent',
'Natural Language :: Chinese (Simplified)',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.2',
'Programming Language :: Python :: 3.3',
'Programming Language :: Python :: 3.4',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7', 'Topic :: Utilities'
], )
tools/infer/predict_det.py
浏览文件 @ 5fc9fff9
......@@ -17,28 +17,32 @@ __dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
import cv2
import copy
import numpy as np
import math
import time
import sys
import paddle.fluid as fluid
import tools.infer.utility as utility
from ppocr.utils.utility import initial_logger
logger = initial_logger()
from ppocr.utils.utility import get_image_file_list, check_and_read_gif
import cv2
from ppocr.data.det.sast_process import SASTProcessTest
from ppocr.data.det.east_process import EASTProcessTest
from ppocr.data.det.db_process import DBProcessTest
from ppocr.postprocess.db_postprocess import DBPostProcess
from ppocr.postprocess.east_postprocess import EASTPostPocess
from ppocr.postprocess.sast_postprocess import SASTPostProcess
import copy
import numpy as np
import math
import time
import sys
class TextDetector(object):
def __init__(self, args):
max_side_len = args.det_max_side_len
self.det_algorithm = args.det_algorithm
self.use_zero_copy_run = args.use_zero_copy_run
preprocess_params = {'max_side_len': max_side_len}
postprocess_params = {}
if self.det_algorithm == "DB":
......@@ -127,7 +131,7 @@ class TextDetector(object):
dt_boxes_new.append(box)
dt_boxes = np.array(dt_boxes_new)
return dt_boxes
def __call__(self, img):
ori_im = img.copy()
im, ratio_list = self.preprocess_op(img)
......@@ -135,8 +139,12 @@ class TextDetector(object):
return None, 0
im = im.copy()
starttime = time.time()
self.input_tensor.copy_from_cpu(im)
self.predictor.zero_copy_run()
if self.use_zero_copy_run:
self.input_tensor.copy_from_cpu(im)
self.predictor.zero_copy_run()
else:
im = fluid.core.PaddleTensor(im)
self.predictor.run([im])
outputs = []
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
......@@ -152,7 +160,7 @@ class TextDetector(object):
outs_dict['f_tvo'] = outputs[3]
else:
outs_dict['maps'] = outputs[0]
dt_boxes_list = self.postprocess_op(outs_dict, [ratio_list])
dt_boxes = dt_boxes_list[0]
if self.det_algorithm == "SAST" and self.det_sast_polygon:
......
tools/infer/predict_rec.py
浏览文件 @ 5fc9fff9
......@@ -17,16 +17,18 @@ __dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
import tools.infer.utility as utility
from ppocr.utils.utility import initial_logger
logger = initial_logger()
from ppocr.utils.utility import get_image_file_list, check_and_read_gif
import cv2
import copy
import numpy as np
import math
import time
import paddle.fluid as fluid
import tools.infer.utility as utility
from ppocr.utils.utility import initial_logger
logger = initial_logger()
from ppocr.utils.utility import get_image_file_list, check_and_read_gif
from ppocr.utils.character import CharacterOps
......@@ -39,6 +41,7 @@ class TextRecognizer(object):
self.rec_batch_num = args.rec_batch_num
self.rec_algorithm = args.rec_algorithm
self.text_len = args.max_text_length
self.use_zero_copy_run = args.use_zero_copy_run
char_ops_params = {
"character_type": args.rec_char_type,
"character_dict_path": args.rec_char_dict_path,
......@@ -199,6 +202,7 @@ class TextRecognizer(object):
norm_img_batch = norm_img_batch.copy()
if self.loss_type == "srn":
starttime = time.time()
encoder_word_pos_list = np.concatenate(encoder_word_pos_list)
gsrm_word_pos_list = np.concatenate(gsrm_word_pos_list)
gsrm_slf_attn_bias1_list = np.concatenate(
......@@ -224,8 +228,13 @@ class TextRecognizer(object):
self.predictor.run(inputs)
else:
self.input_tensor.copy_from_cpu(norm_img_batch)
self.predictor.zero_copy_run()
starttime = time.time()
if self.use_zero_copy_run:
self.input_tensor.copy_from_cpu(norm_img_batch)
self.predictor.zero_copy_run()
else:
norm_img_batch = fluid.core.PaddleTensor(norm_img_batch)
self.predictor.run([norm_img_batch])
if self.loss_type == "ctc":
rec_idx_batch = self.output_tensors[0].copy_to_cpu()
......
tools/infer/predict_system.py
浏览文件 @ 5fc9fff9
......@@ -157,7 +157,6 @@ def main(args):
boxes,
txts,
scores,
draw_txt=True,
drop_score=drop_score)
draw_img_save = "./inference_results/"
if not os.path.exists(draw_img_save):
......
tools/infer/utility.py
浏览文件 @ 5fc9fff9
......@@ -71,6 +71,7 @@ def parse_args():
default="./ppocr/utils/ppocr_keys_v1.txt")
parser.add_argument("--use_space_char", type=bool, default=True)
parser.add_argument("--enable_mkldnn", type=bool, default=False)
parser.add_argument("--use_zero_copy_run", type=bool, default=False)
return parser.parse_args()
......@@ -105,9 +106,12 @@ def create_predictor(args, mode):
#config.enable_memory_optim()
config.disable_glog_info()
# use zero copy
config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
config.switch_use_feed_fetch_ops(False)
if args.use_zero_copy_run:
config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
config.switch_use_feed_fetch_ops(False)
else:
config.switch_use_feed_fetch_ops(True)
predictor = create_paddle_predictor(config)
input_names = predictor.get_input_names()
for name in input_names:
......@@ -140,7 +144,12 @@ def resize_img(img, input_size=600):
return im
def draw_ocr(image, boxes, txts, scores, draw_txt=True, drop_score=0.5):
def draw_ocr(image,
boxes,
txts=None,
scores=None,
drop_score=0.5,
font_path="./doc/simfang.ttf"):
"""
Visualize the results of OCR detection and recognition
args:
......@@ -148,23 +157,29 @@ def draw_ocr(image, boxes, txts, scores, draw_txt=True, drop_score=0.5):
boxes(list): boxes with shape(N, 4, 2)
txts(list): the texts
scores(list): txxs corresponding scores
draw_txt(bool): whether draw text or not
drop_score(float): only scores greater than drop_threshold will be visualized
font_path: the path of font which is used to draw text
return(array):
the visualized img
"""
if scores is None:
scores = [1] * len(boxes)
for (box, score) in zip(boxes, scores):
if score < drop_score or math.isnan(score):
box_num = len(boxes)
for i in range(box_num):
if scores is not None and (scores[i] < drop_score or
math.isnan(scores[i])):
continue
box = np.reshape(np.array(box), [-1, 1, 2]).astype(np.int64)
box = np.reshape(np.array(boxes[i]), [-1, 1, 2]).astype(np.int64)
image = cv2.polylines(np.array(image), [box], True, (255, 0, 0), 2)
if draw_txt:
if txts is not None:
img = np.array(resize_img(image, input_size=600))
txt_img = text_visual(
txts, scores, img_h=img.shape[0], img_w=600, threshold=drop_score)
txts,
scores,
img_h=img.shape[0],
img_w=600,
threshold=drop_score,
font_path=font_path)
img = np.concatenate([np.array(img), np.array(txt_img)], axis=1)
return img
return image
......@@ -242,7 +257,12 @@ def str_count(s):
return s_len - math.ceil(en_dg_count / 2)
def text_visual(texts, scores, img_h=400, img_w=600, threshold=0.):
def text_visual(texts,
scores,
img_h=400,
img_w=600,
threshold=0.,
font_path="./doc/simfang.ttf"):
"""
create new blank img and draw txt on it
args:
......@@ -250,6 +270,7 @@ def text_visual(texts, scores, img_h=400, img_w=600, threshold=0.):
scores(list|None): corresponding score of each txt
img_h(int): the height of blank img
img_w(int): the width of blank img
font_path: the path of font which is used to draw text
return(array):
"""
......@@ -268,7 +289,7 @@ def text_visual(texts, scores, img_h=400, img_w=600, threshold=0.):
font_size = 20
txt_color = (0, 0, 0)
font = ImageFont.truetype("./doc/simfang.ttf", font_size, encoding="utf-8")
font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
gap = font_size + 5
txt_img_list = []
......@@ -349,6 +370,6 @@ if __name__ == '__main__':
txts.append(dic['transcription'])
scores.append(round(dic['scores'], 3))
new_img = draw_ocr(image, boxes, txts, scores, draw_txt=True)
new_img = draw_ocr(image, boxes, txts, scores)
cv2.imwrite(img_name, new_img)
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