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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 project.
The main branch works with PyTorch 1.6+.
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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 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.
While the stable version (0.6.3) and the preview version (1.0.0) are being maintained concurrently now, the former version will be deprecated by the end of 2022. Therefore, we recommend users upgrade to MMOCR 1.0 to fruitful new features and better performance brought by the new architecture. Check out our maintenance plan for how we will maintain them in the future.
v0.6.3 was released in 2022-11-03.
This release enhances the inference script and fixes a bug that might cause failure on TorchServe.
Read Changelog for more details!
A brand new version of MMOCR v1.0.0rc3 was released in 2022-11-03:
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We release several pretrained models using oCLIP-ResNet as the backbone, which is a ResNet variant trained with oCLIP and can significantly boost the performance of text detection models.
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Preparing datasets is troublesome and tedious, especially in OCR domain where multiple datasets are usually required. In order to free our users from laborious work, we designed a Dataset Preparer to help you get a bunch of datasets ready for use, with only one line of command! Dataset Preparer is also crafted to consist of a series of reusable modules, each responsible for handling one of the standardized phases throughout the preparation process, shortening the development cycle on supporting new datasets.
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New engines. MMOCR 1.x is based on MMEngine, which provides a general and powerful runner that allows more flexible customizations and significantly simplifies the entrypoints of high-level interfaces.
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Unified interfaces. As a part of the OpenMMLab 2.0 projects, MMOCR 1.x unifies and refactors the interfaces and internal logics of train, testing, datasets, models, evaluation, and visualization. All the OpenMMLab 2.0 projects share the same design in those interfaces and logics to allow the emergence of multi-task/modality algorithms.
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Cross project calling. Benefiting from the unified design, you can use the models implemented in other OpenMMLab projects, such as MMDet. We provide an example of how to use MMDetection's Mask R-CNN through
MMDetWrapper. Check our documents for more details. More wrappers will be released in the future. -
Stronger visualization. We provide a series of useful tools which are mostly based on brand-new visualizers. As a result, it is more convenient for the users to explore the models and datasets now.
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More documentation and tutorials. We add a bunch of documentation and tutorials to help users get started more smoothly. Read it here.
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One-stop Dataset Preparaion. Multiple datasets are instantly ready with only one line of command, via our Dataset Preparer.
Find more new features in 1.x branch. Issues and PRs are welcome!
MMOCR depends on PyTorch, 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
mim install mmcv-full
mim install mmdet
git clone https://github.com/open-mmlab/mmocr.git
cd mmocr
pip3 install -e .Please see Getting Started for the basic usage of MMOCR.
Supported algorithms:
Text Detection
Text Recognition
Key Information Extraction
- SDMG-R (ArXiv'2021)
Named Entity Recognition
- Bert-Softmax (NAACL'2019)
Please refer to model_zoo for more details.
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{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}
}This project is released under the Apache 2.0 license.
- MMCV: OpenMMLab foundational library for computer vision.
- MIM: MIM installs OpenMMLab packages.
- MMClassification: OpenMMLab image classification toolbox and benchmark.
- MMDetection: OpenMMLab detection toolbox and benchmark.
- MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
- MMRotate: OpenMMLab rotated object detection toolbox and benchmark.
- MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
- MMOCR: OpenMMLab text detection, recognition, and understanding toolbox.
- MMYOLO: OpenMMLab YOLO series toolbox and benchmark.
- MMPose: OpenMMLab pose estimation toolbox and benchmark.
- MMHuman3D: OpenMMLab 3D human parametric model toolbox and benchmark.
- MMSelfSup: OpenMMLab self-supervised learning toolbox and benchmark.
- MMRazor: OpenMMLab model compression toolbox and benchmark.
- MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.
- MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
- MMTracking: OpenMMLab video perception toolbox and benchmark.
- MMFlow: OpenMMLab optical flow toolbox and benchmark.
- MMEditing: OpenMMLab image and video editing toolbox.
- MMGeneration: OpenMMLab image and video generative models toolbox.
- MMDeploy: OpenMMLab model deployment framework.
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