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 1.5+.
Documentation: https://mmocr.readthedocs.io/en/latest/.
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Comprehensive Pipeline
The toolbox supports not only text detection and text recognition, but also their downstream tasks such as key information extraction.
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Multiple Models
The toolbox supports a wide variety of state-of-the-art models for text detection, text recognition and key information extraction.
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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 getting_started.md for how to construct a customized model.
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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.
Supported algorithms:
(click to collapse)
This project is released under the Apache 2.0 license.
If you find this project useful in your research, please consider cite:
@misc{mmocr2021,
title={MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding},
author={MMOCR Contributors},
howpublished = {\url{https://github.com/open-mmlab/mmocr}},
year={2021}
}v0.1.0 was released on 07/04/2021.
Please refer to modelzoo.md for more details.
Please refer to install.md for installation.
Please see getting_started.md for the basic usage of MMOCR.
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.
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