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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 and notes on Branches .
v1.0.0 was released in 2023-04-06. Major updates from 1.0.0rc6 include:
To know more about the updates in MMOCR 1.0, please refer to What's New in MMOCR 1.x, or Read Changelog for more details!
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 project.
The main branch works with PyTorch 1.6+.
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 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.
MMOCR depends on PyTorch, MMEngine, MMCV and MMDetection. Below are quick steps for installation. Please refer to Install Guide for more detailed instruction.
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 .
Please see Quick Run for the basic usage of MMOCR.
Supported algorithms:
BackBonePlease refer to model_zoo for more details.
Here 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.
We appreciate all contributions to improve MMOCR. Please refer to CONTRIBUTING.md for the contributing guidelines.
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.
If you find this project useful in your research, please consider cite:
@article{mmocr2022,
title={MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding},
author={MMOCR Developer Team},
howpublished = {\url{https://github.com/open-mmlab/mmocr}},
year={2022}
}
This project is released under the Apache 2.0 license.
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