mirror of https://github.com/open-mmlab/mmocr.git
[Docs] Update readme according to the guideline (#1047)
* [Docs] Update readme according to the guideline * fix * fix cn linkspull/1022/head^2
Tong Gao
GitHub
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README.md
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README.md
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</sup>
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</div>
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<div> </div>
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</div>
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## Introduction
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English | [简体中文](README_zh-CN.md)
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[](https://github.com/open-mmlab/mmocr/actions)
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[](https://mmocr.readthedocs.io/en/latest/?badge=latest)
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@ -31,13 +26,27 @@ English | [简体中文](README_zh-CN.md)
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[](https://github.com/open-mmlab/mmocr/issues)
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[](https://github.com/open-mmlab/mmocr/issues)
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[📘Documentation](https://mmocr.readthedocs.io/) |
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[🛠️Installation](https://mmocr.readthedocs.io/en/latest/install.html) |
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[👀Model Zoo](https://mmocr.readthedocs.io/en/latest/modelzoo.html) |
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[🆕Update News](https://mmocr.readthedocs.io/en/latest/changelog.html) |
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[🤔Reporting Issues](https://github.com/open-mmlab/mmocr/issues/new/choose)
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</div>
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<div align="center">
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English | [简体中文](README_zh-CN.md)
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</div>
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## Introduction
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MMOCR is an open-source toolbox based on PyTorch and mmdetection for text detection, text recognition, and the corresponding downstream tasks including key information extraction. It is part of the [OpenMMLab](https://openmmlab.com/) project.
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The main branch works with **PyTorch 1.6+**.
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Documentation: https://mmocr.readthedocs.io/en/latest/.
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<div align="left">
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<div align="center">
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<img src="resources/illustration.jpg"/>
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</div>
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@ -59,6 +68,40 @@ Documentation: https://mmocr.readthedocs.io/en/latest/.
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The toolbox provides a comprehensive set of utilities which can help users assess the performance of models. It includes visualizers which allow visualization of images, ground truths as well as predicted bounding boxes, and a validation tool for evaluating checkpoints during training. It also includes data converters to demonstrate how to convert your own data to the annotation files which the toolbox supports.
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## What's New
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v0.6.0 was released in 2022-05-05.
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1. We support [MASTER](https://arxiv.org/abs/1910.02562) and [DBNet++](https://arxiv.org/abs/2202.10304) now!
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2. Three dataset converters are added: LSVT, RCTW, and HierText.
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3. MMOCR now can load data from LMDB dataset. [Doc](https://mmocr.readthedocs.io/en/latest/tools.html#convert-text-recognition-dataset-to-lmdb-format)
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4. We provide a script to convert .json labels obtained by **Labelme** into MMOCR-supported data format.
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Read [Changelog](https://mmocr.readthedocs.io/en/latest/changelog.html) for more details!
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## Installation
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MMOCR depends on [PyTorch](https://pytorch.org/), [MMCV](https://github.com/open-mmlab/mmcv) and [MMDetection](https://github.com/open-mmlab/mmdetection).
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Below are quick steps for installation.
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Please refer to [Install Guide](https://mmocr.readthedocs.io/en/latest/install.html) for more detailed instruction.
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```shell
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conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y
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conda activate open-mmlab
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pip3 install openmim
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mim install mmcv-full
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mim install mmdet
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git clone https://github.com/open-mmlab/mmocr.git
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cd mmocr
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pip3 install -e .
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```
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## Get Started
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Please see [Getting Started](https://mmocr.readthedocs.io/en/latest/getting_started.html) for the basic usage of MMOCR.
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## [Model Zoo](https://mmocr.readthedocs.io/en/latest/modelzoo.html)
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Supported algorithms:
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@ -106,9 +149,14 @@ Supported algorithms:
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Please refer to [model_zoo](https://mmocr.readthedocs.io/en/latest/modelzoo.html) for more details.
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## License
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## Contributing
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This project is released under the [Apache 2.0 license](LICENSE).
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We appreciate all contributions to improve MMOCR. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guidelines.
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## Acknowledgement
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MMOCR is an open-source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks.
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We hope the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new OCR methods.
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## Citation
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@ -123,39 +171,10 @@ If you find this project useful in your research, please consider cite:
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}
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```
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## Changelog
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v0.6.0 was released in 2022-05-05.
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## License
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## Installation
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MMOCR depends on [PyTorch](https://pytorch.org/), [MMCV](https://github.com/open-mmlab/mmcv) and [MMDetection](https://github.com/open-mmlab/mmdetection).
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Below are quick steps for installation.
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Please refer to [Install Guide](https://mmocr.readthedocs.io/en/latest/install.html) for more detailed instruction.
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```shell
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conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y
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conda activate open-mmlab
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pip3 install openmim
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mim install mmcv-full
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mim install mmdet
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git clone https://github.com/open-mmlab/mmocr.git
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cd mmocr
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pip3 install -e .
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```
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## Get Started
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Please see [Getting Started](https://mmocr.readthedocs.io/en/latest/getting_started.html) for the basic usage of MMOCR.
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## Contributing
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We appreciate all contributions to improve MMOCR. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guidelines.
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## Acknowledgement
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MMOCR is an open-source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks.
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We hope the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new OCR methods.
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This project is released under the [Apache 2.0 license](LICENSE).
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## Projects in OpenMMLab
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@ -17,11 +17,6 @@
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</sup>
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</div>
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<div> </div>
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</div>
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## 简介
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[English](/README.md) | 简体中文
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[](https://github.com/open-mmlab/mmocr/actions)
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[](https://mmocr.readthedocs.io/en/latest/?badge=latest)
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@ -31,13 +26,27 @@
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[](https://github.com/open-mmlab/mmocr/issues)
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[](https://github.com/open-mmlab/mmocr/issues)
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[📘文档](https://mmocr.readthedocs.io/zh_CN/latest/) |
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[🛠️安装](https://mmocr.readthedocs.io/zh_CN/latest/install.html) |
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[👀模型库](https://mmocr.readthedocs.io/zh_CN/latest/modelzoo.html) |
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[🆕更新日志](https://mmocr.readthedocs.io/zh_CN/latest/changelog.html) |
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[🤔报告问题](https://github.com/open-mmlab/mmocr/issues/new/choose)
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</div>
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<div align="center">
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[English](/README.md) | 简体中文
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</div>
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## 简介
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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。
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主分支目前支持 **PyTorch 1.6 以上**的版本。
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文档：https://mmocr.readthedocs.io/zh_CN/latest/
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<div align="left">
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<div align="center">
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<img src="resources/illustration.jpg"/>
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</div>
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@ -59,6 +68,38 @@ MMOCR 是基于 PyTorch 和 mmdetection 的开源工具箱，专注于文本检
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-**众多实用工具**
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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。
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## 最新进展
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最新的月度版本 v0.6.0 在 2022.05.05 发布。
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1. 支持了两个新模型：[MASTER](https://arxiv.org/abs/1910.02562) 和 [DBNet++](https://arxiv.org/abs/2202.10304)
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2. 新支持了三个数据集：LSVT, RCTW 和 HierText
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3. 支持从 LMDB 数据集读取数据 [查看文档](https://mmocr.readthedocs.io/en/latest/tools.html#convert-text-recognition-dataset-to-lmdb-format)
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4. 提供了 Labelme 数据格式转换脚本，可使 MMOCR 支持由 Labelme 标注的数据
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阅读[更新日志](https://mmocr.readthedocs.io/en/latest/changelog.html)以获取更多信息。
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## 安装
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MMOCR 依赖 [PyTorch](https://pytorch.org/), [MMCV](https://github.com/open-mmlab/mmcv) 和 [MMDetection](https://github.com/open-mmlab/mmdetection)，以下是安装的简要步骤。
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更详细的安装指南请参考 [安装文档](https://mmocr.readthedocs.io/zh_CN/latest/install.html)。
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```shell
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conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y
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conda activate open-mmlab
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pip3 install openmim
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mim install mmcv-full
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mim install mmdet
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git clone https://github.com/open-mmlab/mmocr.git
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cd mmocr
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pip3 install -e .
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```
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## 快速入门
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请参考[快速入门](https://mmocr.readthedocs.io/zh_CN/latest/getting_started.html)文档学习 MMOCR 的基本使用。
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## [模型库](https://mmocr.readthedocs.io/en/latest/modelzoo.html)
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支持的算法：
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@ -106,9 +147,12 @@ MMOCR 是基于 PyTorch 和 mmdetection 的开源工具箱，专注于文本检
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请点击[模型库](https://mmocr.readthedocs.io/en/latest/modelzoo.html)查看更多关于上述算法的详细信息。
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## 开源许可证
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## 贡献指南
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该项目采用 [Apache 2.0 license](LICENSE) 开源许可证。
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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](.github/CONTRIBUTING.md)来了解参与项目贡献的相关指引。
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## 致谢
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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。
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## 引用
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@ -123,40 +167,13 @@ MMOCR 是基于 PyTorch 和 mmdetection 的开源工具箱，专注于文本检
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}
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```
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## 更新日志
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## 开源许可证
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最新的月度版本 v0.6.0 在 2022.05.05 发布。
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该项目采用 [Apache 2.0 license](LICENSE) 开源许可证。
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## 安装
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MMOCR 依赖 [PyTorch](https://pytorch.org/), [MMCV](https://github.com/open-mmlab/mmcv) 和 [MMDetection](https://github.com/open-mmlab/mmdetection)，以下是安装的简要步骤。
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更详细的安装指南请参考 [安装文档](https://mmocr.readthedocs.io/zh_CN/latest/install.html)。
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```shell
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conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y
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conda activate open-mmlab
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pip3 install openmim
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mim install mmcv-full
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mim install mmdet
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git clone https://github.com/open-mmlab/mmocr.git
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cd mmocr
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pip3 install -e .
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```
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## 快速入门
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请参考[快速入门](https://mmocr.readthedocs.io/zh_CN/latest/getting_started.html)文档学习 MMOCR 的基本使用。
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## 贡献指南
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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](.github/CONTRIBUTING.md)来了解参与项目贡献的相关指引。
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## 致谢
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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。
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## OpenMMLab 的其他项目
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- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库
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- [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMlab 项目、算法、模型的统一入口
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- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱
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