Mmocr

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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 and notes on Branches .

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, or Read Changelog 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 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 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, 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 .

Get Started

Please see Quick Run for the basic usage of MMOCR.

Model Zoo

Supported algorithms:

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

• [x] oCLIP (ECCV'2022)

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

• [x] DBNet (AAAI'2020) / DBNet++ (TPAMI'2022)
• [x] Mask R-CNN (ICCV'2017)
• [x] PANet (ICCV'2019)
• [x] PSENet (CVPR'2019)
• [x] TextSnake (ECCV'2018)
• [x] DRRG (CVPR'2020)
• [x] FCENet (CVPR'2021)

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

• [x] ABINet (CVPR'2021)
• [x] ASTER (TPAMI'2018)
• [x] CRNN (TPAMI'2016)
• [x] MASTER (PR'2021)
• [x] NRTR (ICDAR'2019)
• [x] RobustScanner (ECCV'2020)
• [x] SAR (AAAI'2019)
• [x] SATRN (CVPR'2020 Workshop on Text and Documents in the Deep Learning Era)
• [x] SVTR (IJCAI'2022)

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

• [x] SDMG-R (ArXiv'2021)

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

• [x] ABCNet (CVPR'2020)
• [x] ABCNetV2 (TPAMI'2021)
• [x] SPTS (ACM MM'2022)

</details>

Please refer to model_zoo for more details.

Projects

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.

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 fe