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[📘Documentation](https://mmocr.readthedocs.io/en/dev-1.x/) |
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[👀Model Zoo](https://mmocr.readthedocs.io/en/dev-1.x/modelzoo.html) |
[🆕Update News](https://mmocr.readthedocs.io/en/dev-1.x/notes/changelog.html) |
[🤔Reporting Issues](https://github.com/open-mmlab/mmocr/issues/new/choose)

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English | [简体中文](README_zh-CN.md)

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## Latest Updates

**The default branch is now `main` and the code on the branch has been upgraded to v1.0.0. The old `main` branch (v0.6.3) code now exists on the `0.x` branch.** If you have been using the `main` branch and encounter upgrade issues, please read the [Migration Guide](https://mmocr.readthedocs.io/en/dev-1.x/migration/overview.html) and notes on [Branches](https://mmocr.readthedocs.io/en/dev-1.x/migration/branches.html) .

v1.0.0 was released in 2023-04-06. Major updates from 1.0.0rc6 include:

1. Support for SCUT-CTW1500, SynthText, and MJSynth datasets in Dataset Preparer
2. Updated FAQ and documentation
3. Deprecation of file_client_args in favor of backend_args
4. Added a new MMOCR tutorial notebook

To know more about the updates in MMOCR 1.0, please refer to [What's New in MMOCR 1.x](https://mmocr.readthedocs.io/en/dev-1.x/migration/news.html), or
Read [Changelog](https://mmocr.readthedocs.io/en/dev-1.x/notes/changelog.html) for more details!

## Introduction

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.

The main branch works with **PyTorch 1.6+**.

<div align="center">
  <img src="https://user-images.githubusercontent.com/24622904/187838618-1fdc61c0-2d46-49f9-8502-976ffdf01f28.png"/>
</div>

### Major Features

- **Comprehensive Pipeline**

  The toolbox supports not only text detection and text recognition, but also their downstream tasks such as key information extraction.

- **Multiple Models**

  The toolbox supports a wide variety of state-of-the-art models for text detection, text recognition and key information extraction.

- **Modular Design**

  The modular design of MMOCR enables users to define their own optimizers, data preprocessors, and model components such as backbones, necks and heads as well as losses. Please refer to [Overview](https://mmocr.readthedocs.io/en/dev-1.x/get_started/overview.html) for how to construct a customized model.

- **Numerous Utilities**

  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.

## Installation

MMOCR depends on [PyTorch](https://pytorch.org/), [MMEngine](https://github.com/open-mmlab/mmengine), [MMCV](https://github.com/open-mmlab/mmcv) and [MMDetection](https://github.com/open-mmlab/mmdetection).
Below are quick steps for installation.
Please refer to [Install Guide](https://mmocr.readthedocs.io/en/dev-1.x/get_started/install.html) for more detailed instruction.

```shell
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
git clone https://github.com/open-mmlab/mmocr.git
cd mmocr
mim install -e .
```

## Get Started

Please see [Quick Run](https://mmocr.readthedocs.io/en/dev-1.x/get_started/quick_run.html) for the basic usage of MMOCR.

## [Model Zoo](https://mmocr.readthedocs.io/en/dev-1.x/modelzoo.html)

Supported algorithms:

<details open>
<summary>BackBone</summary>

- [x] [oCLIP](configs/backbone/oclip/README.md) (ECCV'2022)

</details>

<details open>
<summary>Text Detection</summary>

- [x] [DBNet](configs/textdet/dbnet/README.md) (AAAI'2020) / [DBNet++](configs/textdet/dbnetpp/README.md) (TPAMI'2022)
- [x] [Mask R-CNN](configs/textdet/maskrcnn/README.md) (ICCV'2017)
- [x] [PANet](configs/textdet/panet/README.md) (ICCV'2019)
- [x] [PSENet](configs/textdet/psenet/README.md) (CVPR'2019)
- [x] [TextSnake](configs/textdet/textsnake/README.md) (ECCV'2018)
- [x] [DRRG](configs/textdet/drrg/README.md) (CVPR'2020)
- [x] [FCENet](configs/textdet/fcenet/README.md) (CVPR'2021)

</details>

<details open>
<summary>Text Recognition</summary>

- [x] [ABINet](configs/textrecog/abinet/README.md) (CVPR'2021)
- [x] [ASTER](configs/textrecog/aster/README.md) (TPAMI'2018)
- [x] [CRNN](configs/textrecog/crnn/README.md) (TPAMI'2016)
- [x] [MASTER](configs/textrecog/master/README.md) (PR'2021)
- [x] [NRTR](configs/textrecog/nrtr/README.md) (ICDAR'2019)
- [x] [RobustScanner](configs/textrecog/robust_scanner/README.md) (ECCV'2020)
- [x] [SAR](configs/textrecog/sar/README.md) (AAAI'2019)
- [x] [SATRN](configs/textrecog/satrn/README.md) (CVPR'2020 Workshop on Text and Documents in the Deep Learning Era)
- [x] [SVTR](configs/textrecog/svtr/README.md) (IJCAI'2022)

</details>

<details open>
<summary>Key Information Extraction</summary>

- [x] [SDMG-R](configs/kie/sdmgr/README.md) (ArXiv'2021)

</details>

<details open>
<summary>Text Spotting</summary>

- [x] [ABCNet](projects/ABCNet/README.md) (CVPR'2020)
- [x] [ABCNetV2](projects/ABCNet/README_V2.md) (TPAMI'2021)
- [x] [SPTS](projects/SPTS/README.md) (ACM MM'2022)

</details>

Please refer to [model_zoo](https://mmocr.readthedocs.io/en/dev-1.x/modelzoo.html) for more details.

## Projects

[Here](projects/README.md) are some implementations of SOTA models and solutions built on MMOCR, which are supported and maintained by community users. These projects demonstrate the best practices based on MMOCR for research and product development. We welcome and appreciate all the contributions to OpenMMLab ecosystem.

## Contributing

We appreciate all contributions to improve MMOCR. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guidelines.

## Acknowledgement

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.
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.

## Citation

If you find this project useful in your research, please consider cite:

```bibtex
@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}
}
```

## License

This project is released under the [Apache 2.0 license](LICENSE).

## OpenMMLab Family

- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab foundational library for training deep learning models
- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.

## Welcome to the OpenMMLab community

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