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README
Apache-2.0

English | 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,助力开发者训练出更好的模型,并应用落地。

近期更新

  • 2021.12.21《动手学OCR · 十讲》课程开讲,12月21日起每晚八点半线上授课!免费报名地址
  • 2021.12.21 发布PaddleOCR v2.4。OCR算法新增1种文本检测算法(PSENet),3种文本识别算法(NRTR、SEED、SAR);文档结构化算法新增1种关键信息提取算法(SDMGR,文档),3种DocVQA算法(LayoutLM、LayoutLMv2,LayoutXLM,文档)。
  • PaddleOCR研发团队对最新发版内容技术深入解读,9月8日晚上20:15,课程回放
  • 2021.9.7 发布PaddleOCR v2.3与PP-OCRv2,CPU推理速度相比于PP-OCR server提升220%;效果相比于PP-OCR mobile 提升7%。
  • 2021.8.3 发布PaddleOCR v2.2,新增文档结构分析PP-Structure工具包,支持版面分析与表格识别(含Excel导出)。

更多

特性

  • PP-OCR系列高质量预训练模型,准确的识别效果
    • 超轻量PP-OCRv2系列:检测(3.1M)+ 方向分类器(1.4M)+ 识别(8.5M)= 13.0M
    • 超轻量PP-OCR mobile移动端系列:检测(3.0M)+方向分类器(1.4M)+ 识别(5.0M)= 9.4M
    • 通用PPOCR server系列:检测(47.1M)+方向分类器(1.4M)+ 识别(94.9M)= 143.4M
    • 支持中英文数字组合识别、竖排文本识别、长文本识别
    • 支持多语言识别:韩语、日语、德语、法语等约80种语言
  • PP-Structure文档结构化系统
    • 支持版面分析与表格识别(含Excel导出)
    • 支持关键信息提取任务
    • 支持DocVQA任务
  • 丰富易用的OCR相关工具组件
    • 半自动数据标注工具PPOCRLabel:支持快速高效的数据标注
    • 数据合成工具Style-Text:批量合成大量与目标场景类似的图像
  • 支持用户自定义训练,提供丰富的预测推理部署方案
  • 支持PIP快速安装使用
  • 可运行于Linux、Windows、MacOS等多种系统

上述内容的使用方法建议从文档教程中的快速开始体验

社区、社区贡献与社区常规赛

  • 加入社区:微信扫描下方二维码加入官方交流群,与各行各业开发者充分交流,期待您的加入。
  • 社区贡献:社区贡献文档中包含了社区用户使用PaddleOCR开发的各种工具、应用以及为PaddleOCR贡献的功能、优化的文档与代码等,是官方为社区开发者打造的荣誉墙、也是帮助优质项目宣传的广播站。如果您的OCR项目未被收集在文档中,可根据文档说明与我们联系。最新社区贡献可查看此处
  • 社区常规赛:作为社区贡献的具体承载形式,社区常规赛是面向OCR开发者的积分赛事。首届社区常规赛与《动手学OCR · 十讲》课程联合推广。社区常规赛的赛题详情与报名方法可参考链接

零代码体验

PP-OCR系列模型列表(更新中)

模型简介 模型名称 推荐场景 检测模型 方向分类器 识别模型
中英文超轻量PP-OCRv2模型(13.0M) ch_PP-OCRv2_xx 移动端&服务器端 推理模型 / 训练模型 推理模型 / 预训练模型 推理模型 / 训练模型
中英文超轻量PP-OCR mobile模型(9.4M) ch_ppocr_mobile_v2.0_xx 移动端&服务器端 推理模型 / 预训练模型 推理模型 / 预训练模型 推理模型 / 预训练模型
中英文通用PP-OCR server模型(143.4M) ch_ppocr_server_v2.0_xx 服务器端 推理模型 / 预训练模型 推理模型 / 预训练模型 推理模型 / 预训练模型

更多模型下载(包括多语言),可以参考PP-OCR 系列模型下载

文档教程

PP-OCRv2 Pipeline

[1] PP-OCR是一个实用的超轻量OCR系统。主要由DB文本检测、检测框矫正和CRNN文本识别三部分组成。该系统从骨干网络选择和调整、预测头部的设计、数据增强、学习率变换策略、正则化参数选择、预训练模型使用以及模型自动裁剪量化8个方面,采用19个有效策略,对各个模块的模型进行效果调优和瘦身(如绿框所示),最终得到整体大小为3.5M的超轻量中英文OCR和2.8M的英文数字OCR。更多细节请参考PP-OCR技术方案 https://arxiv.org/abs/2009.09941

[2] PP-OCRv2在PP-OCR的基础上,进一步在5个方面重点优化,检测模型采用CML协同互学习知识蒸馏策略和CopyPaste数据增广策略;识别模型采用LCNet轻量级骨干网络、UDML 改进知识蒸馏策略和Enhanced CTC loss损失函数改进(如上图红框所示),进一步在推理速度和预测效果上取得明显提升。更多细节请参考PP-OCRv2技术报告

效果展示 more

  • 中文模型
  • 英文模型
  • 其他语言模型

最新社区贡献

完整社区贡献列表可查看社区贡献文档

许可证书

本项目的发布受Apache 2.0 license许可认证。

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简介

Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices) 展开 收起
Jupyter Notebook 等 6 种语言
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