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📘文档 | 🛠️安装 | 👀模型库 | 🆕更新日志 | 🤔报告问题

English | 简体中文

近期更新

默认分支已经从 main 切换到 1.x。我们鼓励用户迁移到最新版本,请参考 迁移指南 以了解更多细节。

最新的版本 v1.0.0rc6 于 2023-03-07 发布。

  1. projects/ 目录中新增了 ABCNet v2 (仅支持推理) 和 SPTS 模型;

  2. 新增统一推理接口 Inferencer,用户可以方便直接地进行各任务的推理。文档

  3. 支持了文本识别任务的测试时数据增强。文档

  4. 通过 BatchAugSampler 支持了 batch augmentation ,这是 SPTS 中使用的一种技巧。

  5. 重构了 Dataset Preparer ,用户现在可以更灵活地配置数据集的预处理流程。除此之外,用户现在也可以将文本识别数据集转换为 LMDB 格式。文档

  6. 修正了一些端到端数据集的标注,保证了数据集的正确性及与常见实践的一致性。

  7. 减少了 shapely 中可能出现的一些错误警告。

阅读更新日志以获取更多信息。

简介

MMOCR 是基于 PyTorch 和 mmdetection 的开源工具箱,专注于文本检测,文本识别以及相应的下游任务,如关键信息提取。 它是 OpenMMLab 项目的一部分。

主分支目前支持 PyTorch 1.6 以上的版本。

主要特性

-全流程

该工具箱不仅支持文本检测和文本识别,还支持其下游任务,例如关键信息提取。

-多种模型

该工具箱支持用于文本检测,文本识别和关键信息提取的各种最新模型。

-模块化设计

MMOCR 的模块化设计使用户可以定义自己的优化器,数据预处理器,模型组件如主干模块,颈部模块和头部模块,以及损失函数。有关如何构建自定义模型的信息,请参考概览

-众多实用工具

该工具箱提供了一套全面的实用程序,可以帮助用户评估模型的性能。它包括可对图像,标注的真值以及预测结果进行可视化的可视化工具,以及用于在训练过程中评估模型的验证工具。它还包括数据转换器,演示了如何将用户自建的标注数据转换为 MMOCR 支持的标注文件。

MMOCR 1.0 更新汇总

  1. 架构升级:MMOCR 1.x 是基于 MMEngine,提供了一个通用的、强大的执行器,允许更灵活的定制,提供了统一的训练和测试入口。

  2. 统一接口:MMOCR 1.x 统一了数据集、模型、评估和可视化的接口和内部逻辑。支持更强的扩展性。

  3. 跨项目调用:受益于统一的设计,你可以使用其他OpenMMLab项目中实现的模型,如MMDet。 我们提供了一个例子,说明如何通过MMDetWrapper使用MMDetection的Mask R-CNN。查看我们的文档以了解更多细节。更多的包装器将在未来发布。

  4. 更强的可视化:我们提供了一系列可视化工具, 用户现在可以更方便可视化数据。

  5. 更多的文档和教程:我们增加了更多的教程,降低用户的学习门槛。详见教程

  6. 一站式数据准备:准备数据集已经不再是难事。使用我们的 Dataset Preparer,一行命令即可让多个数据集准备就绪。

  7. 拥抱更多 projects/: 我们推出了 projects/ 文件夹,用于存放一些实验性的新特性、框架和模型。我们对这个文件夹下的代码规范不作过多要求,力求让社区的所有想法第一时间得到实现和展示。请查看我们的样例 project 以了解更多。

  8. 更多新模型:MMOCR 1.0 支持了更多模型和模型种类。

安装

MMOCR 依赖 PyTorch, MMEngine, MMCVMMDetection,以下是安装的简要步骤。 更详细的安装指南请参考 安装文档

conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y
conda activate open-mmlab
pip3 install openmim
mim install mmengine
mim install 'mmcv>=2.0.0rc1'
mim install 'mmdet>=3.0.0rc0'
git clone https://github.com/open-mmlab/mmocr.git
cd mmocr
git checkout 1.x
pip3 install -e .

快速入门

请参考快速入门文档学习 MMOCR 的基本使用。

模型库

支持的算法:

骨干网络
文字检测
文字识别
关键信息提取
端对端 OCR

请点击模型库查看更多关于上述算法的详细信息。

贡献指南

我们感谢所有的贡献者为改进和提升 MMOCR 所作出的努力。请参考贡献指南来了解参与项目贡献的相关指引。

致谢

MMOCR 是一款由来自不同高校和企业的研发人员共同参与贡献的开源项目。我们感谢所有为项目提供算法复现和新功能支持的贡献者,以及提供宝贵反馈的用户。 我们希望此工具箱可以帮助大家来复现已有的方法和开发新的方法,从而为研究社区贡献力量。

引用

如果您发现此项目对您的研究有用,请考虑引用:

@article{mmocr2021,
    title={MMOCR:  A Comprehensive Toolbox for Text Detection, Recognition and Understanding},
    author={Kuang, Zhanghui and Sun, Hongbin and Li, Zhizhong and Yue, Xiaoyu and Lin, Tsui Hin and Chen, Jianyong and Wei, Huaqiang and Zhu, Yiqin and Gao, Tong and Zhang, Wenwei and Chen, Kai and Zhang, Wayne and Lin, Dahua},
    journal= {arXiv preprint arXiv:2108.06543},
    year={2021}
}

开源许可证

该项目采用 Apache 2.0 license 开源许可证。

OpenMMLab 的其他项目

  • MMEngine: OpenMMLab 深度学习模型训练基础库
  • MMCV: OpenMMLab 计算机视觉基础库
  • MIM: MIM 是 OpenMMlab 项目、算法、模型的统一入口
  • MMClassification: OpenMMLab 图像分类工具箱
  • MMDetection: OpenMMLab 目标检测工具箱
  • MMDetection3D: OpenMMLab 新一代通用 3D 目标检测平台
  • MMRotate: OpenMMLab 旋转框检测工具箱与测试基准
  • MMSegmentation: OpenMMLab 语义分割工具箱
  • MMOCR: OpenMMLab 全流程文字检测识别理解工具箱
  • MMPose: OpenMMLab 姿态估计工具箱
  • MMHuman3D: OpenMMLab 人体参数化模型工具箱与测试基准
  • MMSelfSup: OpenMMLab 自监督学习工具箱与测试基准
  • MMRazor: OpenMMLab 模型压缩工具箱与测试基准
  • MMFewShot: OpenMMLab 少样本学习工具箱与测试基准
  • MMAction2: OpenMMLab 新一代视频理解工具箱
  • MMTracking: OpenMMLab 一体化视频目标感知平台
  • MMFlow: OpenMMLab 光流估计工具箱与测试基准
  • MMEditing: OpenMMLab 图像视频编辑工具箱
  • MMGeneration: OpenMMLab 图片视频生成模型工具箱
  • MMDeploy: OpenMMLab 模型部署框架

欢迎加入 OpenMMLab 社区

扫描下方的二维码可关注 OpenMMLab 团队的 知乎官方账号,加入 OpenMMLab 团队的 官方交流 QQ 群,或通过添加微信“Open小喵Lab”加入官方交流微信群。

我们会在 OpenMMLab 社区为大家

  • 📢 分享 AI 框架的前沿核心技术
  • 💻 解读 PyTorch 常用模块源码
  • 📰 发布 OpenMMLab 的相关新闻
  • 🚀 介绍 OpenMMLab 开发的前沿算法
  • 🏃 获取更高效的问题答疑和意见反馈
  • 🔥 提供与各行各业开发者充分交流的平台

干货满满 📘,等你来撩 💗,OpenMMLab 社区期待您的加入 👬

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简介

基于 PyTorch 和 MMDetection 的开源工具箱,支持众多 OCR 相关的模型,涵盖了文本检测、文本识别以及关键信息提取等多个主要方向。它同时还支持了大多数流行的学术数据集,并提供了许多实用工具帮助用户评估模型的性能。 展开 收起
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