Skip to content

Latest commit

 

History

History
240 lines (167 loc) · 13.6 KB

README.md

File metadata and controls

240 lines (167 loc) · 13.6 KB
Raw

English | 简体中文

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 project.

The main branch works with PyTorch 1.6+.

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 Getting Started 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.

What's New

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.

💎 Stable version

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!

🌟 Preview of 1.x version

A brand new version of MMOCR v1.0.0rc3 was released in 2022-11-03:

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. More documentation and tutorials. We add a bunch of documentation and tutorials to help users get started more smoothly. Read it here.

  8. 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!

Installation

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 .

Get Started

Please see Getting Started for the basic usage of MMOCR.

Model Zoo

Supported algorithms:

Text Detection
Text Recognition
Key Information Extraction
Named Entity Recognition

Please refer to model_zoo for more details.

Contributing

We appreciate all contributions to improve MMOCR. Please refer to 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:

@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.

Projects in OpenMMLab

  • 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.

Welcome to the OpenMMLab community

Scan the QR code below to follow the OpenMMLab team's Zhihu Official Account and join the OpenMMLab team's QQ Group, or join the official communication WeChat group by adding the WeChat, or join our Slack

We will provide you with the OpenMMLab community

  • 📢 share the latest core technologies of AI frameworks
  • 💻 Explaining PyTorch common module source Code
  • 📰 News related to the release of OpenMMLab
  • 🚀 Introduction of cutting-edge algorithms developed by OpenMMLab 🏃 Get the more efficient answer and feedback
  • 🔥 Provide a platform for communication with developers from all walks of life

The OpenMMLab community looks forward to your participation! 👬