mmocr
OpenMMLab Text Detection, Recognition and Understanding Toolbox
Science Score: 54.0%
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Low similarity (10.1%) to scientific vocabulary
Keywords
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Repository
OpenMMLab Text Detection, Recognition and Understanding Toolbox
Basic Info
- Host: GitHub
- Owner: open-mmlab
- License: apache-2.0
- Language: Python
- Default Branch: main
- Homepage: https://mmocr.readthedocs.io/en/dev-1.x/
- Size: 15.8 MB
Statistics
- Stars: 4,614
- Watchers: 60
- Forks: 772
- Open Issues: 193
- Releases: 20
Topics
Metadata Files
README.md
[📘Documentation](https://mmocr.readthedocs.io/en/dev-1.x/) |
[🛠️Installation](https://mmocr.readthedocs.io/en/dev-1.x/get_started/install.html) |
[👀Model Zoo](https://mmocr.readthedocs.io/en/dev-1.x/modelzoo.html) |
[🆕Update News](https://mmocr.readthedocs.io/en/dev-1.x/notes/changelog.html) |
[🤔Reporting Issues](https://github.com/open-mmlab/mmocr/issues/new/choose)
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:
- Support for SCUT-CTW1500, SynthText, and MJSynth datasets in Dataset Preparer
- Updated FAQ and documentation
- Deprecation of fileclientargs in favor of backend_args
- 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+.
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.
shell
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:
BackBone
- [x] [oCLIP](configs/backbone/oclip/README.md) (ECCV'2022)Text Detection
- [x] [DBNet](configs/textdet/dbnet/README.md) (AAAI'2020) / [DBNet++](configs/textdet/dbnetpp/README.md) (TPAMI'2022) - [x] [Mask R-CNN](configs/textdet/maskrcnn/README.md) (ICCV'2017) - [x] [PANet](configs/textdet/panet/README.md) (ICCV'2019) - [x] [PSENet](configs/textdet/psenet/README.md) (CVPR'2019) - [x] [TextSnake](configs/textdet/textsnake/README.md) (ECCV'2018) - [x] [DRRG](configs/textdet/drrg/README.md) (CVPR'2020) - [x] [FCENet](configs/textdet/fcenet/README.md) (CVPR'2021)Text Recognition
- [x] [ABINet](configs/textrecog/abinet/README.md) (CVPR'2021) - [x] [ASTER](configs/textrecog/aster/README.md) (TPAMI'2018) - [x] [CRNN](configs/textrecog/crnn/README.md) (TPAMI'2016) - [x] [MASTER](configs/textrecog/master/README.md) (PR'2021) - [x] [NRTR](configs/textrecog/nrtr/README.md) (ICDAR'2019) - [x] [RobustScanner](configs/textrecog/robust_scanner/README.md) (ECCV'2020) - [x] [SAR](configs/textrecog/sar/README.md) (AAAI'2019) - [x] [SATRN](configs/textrecog/satrn/README.md) (CVPR'2020 Workshop on Text and Documents in the Deep Learning Era) - [x] [SVTR](configs/textrecog/svtr/README.md) (IJCAI'2022)Key Information Extraction
- [x] [SDMG-R](configs/kie/sdmgr/README.md) (ArXiv'2021)Text Spotting
- [x] [ABCNet](projects/ABCNet/README.md) (CVPR'2020) - [x] [ABCNetV2](projects/ABCNet/README_V2.md) (TPAMI'2021) - [x] [SPTS](projects/SPTS/README.md) (ACM MM'2022)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 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:
bibtex
@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}
}
License
This project is released under the Apache 2.0 license.
OpenMMLab Family
- MMEngine: OpenMMLab foundational library for training deep learning models
- 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.
- 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! 👬
Owner
- Name: OpenMMLab
- Login: open-mmlab
- Kind: organization
- Location: China
- Website: https://openmmlab.com
- Twitter: OpenMMLab
- Repositories: 53
- Profile: https://github.com/open-mmlab
Citation (CITATION.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." title: "OpenMMLab Text Detection, Recognition and Understanding Toolbox" authors: - name: "MMOCR Contributors" version: 0.3.0 date-released: 2020-08-15 repository-code: "https://github.com/open-mmlab/mmocr" license: Apache-2.0
GitHub Events
Total
- Issues event: 5
- Watch event: 291
- Issue comment event: 17
- Push event: 1
- Fork event: 38
Last Year
- Issues event: 5
- Watch event: 291
- Issue comment event: 17
- Push event: 1
- Fork event: 38
Committers
Last synced: about 1 year ago
Top Committers
| Name | Commits | |
|---|---|---|
| Tong Gao | g****o@g****m | 397 |
| liukuikun | 2****k | 149 |
| Xinyu Wang | 4****g | 127 |
| Hongbin Sun | h****6@g****m | 114 |
| jiangqing.vendor | m****n@o****m | 89 |
| lizz | i****e | 68 |
| quincylin1 | 3****1 | 23 |
| Kevin Wang | 3****u | 10 |
| AllentDan | 4****n | 9 |
| Jianyong Chen | 4****6 | 9 |
| yuexy | y****y | 6 |
| jeffreykuang | k****h@g****m | 5 |
| Zyq-scut | 6****t | 5 |
| vansin | m****e@1****m | 4 |
| liukuikun | w****5 | 4 |
| shaohua.zhang | B****f | 3 |
| garvan2021 | 8****1 | 3 |
| #W[_t | d****7@g****m | 3 |
| Sergio Bugallo Enjamio | s****o@c****s | 3 |
| SamAyala | 7****2 | 3 |
| sunhongbin | s****n@s****m | 3 |
| Yewen Zhou | y****u@b****u | 2 |
| Yang Liu | 6****3 | 2 |
| RangiLyu | l****q@g****m | 2 |
| Max Bachmann | k****t@m****e | 2 |
| LvTianlei | 2****3@q****m | 2 |
| Janghoo Lee | 4****n | 2 |
| Hugo Tong | t****5@g****m | 2 |
| EricTian | 4****8@q****m | 1 |
| EnableAsync | 4****c | 1 |
| and 56 more... | ||
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 151
- Total pull requests: 105
- Average time to close issues: 28 days
- Average time to close pull requests: 15 days
- Total issue authors: 124
- Total pull request authors: 68
- Average comments per issue: 2.3
- Average comments per pull request: 2.49
- Merged pull requests: 62
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 7
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 7
- Pull request authors: 0
- Average comments per issue: 0.14
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- wangzixiang99 (7)
- OuXiao514 (4)
- mazharsaif (2)
- anbo724 (2)
- YaohouF (2)
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- pekopoke (2)
- R4Ajeti (2)
- wangjiale1998 (2)
- xiaomaofeng (2)
- MichaelChao02 (2)
- srcjun (2)
- CyanMystery (2)
- Hegelim (2)
- yCobanoglu (2)
Pull Request Authors
- gaotongxiao (13)
- Mountchicken (8)
- KevinNuNu (6)
- A-new-b (5)
- Harold-lkk (4)
- LRJKD (2)
- shiyunalex (2)
- Howardzhangdqs (2)
- frankstorming (2)
- GangsuKim (2)
- jeffreykuang (2)
- hugotong6425 (2)
- MiXaiLL76 (2)
- AK391 (1)
- TekayaNidham (1)
Top Labels
Issue Labels
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Packages
- Total packages: 3
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Total downloads:
- pypi 8,623 last-month
-
Total dependent packages: 3
(may contain duplicates) -
Total dependent repositories: 37
(may contain duplicates) - Total versions: 40
- Total maintainers: 2
pypi.org: mmocr
OpenMMLab Text Detection, OCR, and NLP Toolbox
- Homepage: https://github.com/open-mmlab/mmocr
- Documentation: https://mmocr.readthedocs.io/
- License: Apache License 2.0
-
Latest release: 1.0.1
published over 2 years ago
Rankings
Maintainers (1)
proxy.golang.org: github.com/open-mmlab/mmocr
- Documentation: https://pkg.go.dev/github.com/open-mmlab/mmocr#section-documentation
- License: apache-2.0
-
Latest release: v1.0.1
published over 2 years ago
Rankings
pypi.org: ocr-jm
Modification on top of OpenMMLab Text Detection, OCR, and NLP Toolbox
- Homepage: https://github.com/NaJaeMin92
- Documentation: https://ocr-jm.readthedocs.io/
- License: Apache License 2.0
Rankings
Maintainers (1)
Dependencies
- asynctest * test
- codecov * test
- flake8 * test
- isort * test
- kwarray * test
- pytest * test
- pytest-cov * test
- pytest-runner * test
- ubelt * test
- xdoctest >=0.10.0 test
- yapf * test
- recommonmark *
- sphinx *
- sphinx_markdown_tables *
- sphinx_rtd_theme *
- albumentations >=1.1.0
- numpy *
- pyclipper *
- torch >=1.1
- docutils ==0.16.0
- markdown >=3.4.0
- myst-parser *
- sphinx ==4.0.2
- sphinx_copybutton *
- sphinx_markdown_tables >=0.0.16
- mmcv-full >=1.3.8
- mmdet >=2.21.0
- imgaug *
- kwarray *
- lanms-neo ==1.0.2
- lmdb *
- matplotlib *
- mmcv *
- mmdet *
- pyclipper *
- rapidfuzz >=2.0.0
- regex *
- scikit-image *
- scipy *
- shapely *
- titlecase *
- torch *
- torchvision *
- imgaug *
- lanms-neo ==1.0.2
- lmdb *
- matplotlib *
- numpy *
- opencv-python >=4.2.0.32,
- pyclipper *
- pycocotools *
- rapidfuzz >=2.0.0
- scikit-image *