View mmocr on Snyk Open Source Advisor

Package Health Score

59 / 100

security
Security review needed
popularity
Recognized
maintenance
Inactive
community
Active

Explore Similar Packages

Popularity

Recognized

GitHub Stars: 4.39K
Forks: 756
Contributors: 90

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package mmocr, we found that it has been starred 4,386 times.

Security

Security review needed

Security and license risk for latest version

Release Date: Jul 4, 2023
Direct Vulnerabilities: 0
C
0
H
0
M
0
L
Indirect Vulnerabilities: 0
C
0
H
0
M
0
L
License Risk: 0
H
0
M
0
L

All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

Apache-2.0

Security Policy

We found a way for you to contribute to the project! Looks like mmocr is missing a security policy.

You can connect your project's repository to Snyk to stay up to date on security alerts and receive automatic fix pull requests.

Keep your project free of vulnerabilities with Snyk

Maintenance

Inactive

Commit Frequency

Open Issues: 159
Open PR: 34
Last Release: 1 year ago
Last Commit: 28 days ago

Further analysis of the maintenance status of mmocr based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Inactive.

An important project maintenance signal to consider for mmocr is that it hasn't seen any new versions released to PyPI in the past 12 months, and could be considered as a discontinued project, or that which receives low attention from its maintainers.

As a healthy sign for on-going project maintenance, we found that the GitHub repository had at least 1 pull request or issue interacted with by the community.

Community

Active

Readme: Yes
Contributing.md: Yes
Code of Conduct: Yes
Contributors: 90
Funding: No

With more than 10 contributors for the mmocr repository, this is possibly a sign for a growing and inviting community.

Embed Package Health Score Badge

Keep your project healthy

Check your requirements.txt

Ensure all the packages you're using are healthy and well-maintained

Snyk Vulnerability Scanner

Get health score & security insights directly in your IDE

Package

Python Versions Compatibility: ==3.6.*, ==3.7.*, ==3.8.*, ==3.9.*

Age: 4 years
Latest Release: 1 year ago
Dependencies: 9 Direct / 21 Total
Versions: 19
Maintainers: 0
Wheels: OS Independent

Readme

OpenMMLab website ^HOT OpenMMLab platform ^{TRY IT OUT}

📘Documentation | 🛠️Installation | 👀Model Zoo | 🆕Update News | 🤔Reporting Issues

English | 简体中文

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 file_client_args 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.

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 (ECCV'2022)

Text Detection

[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)

Text Recognition

[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)

Key Information Extraction

[x] SDMG-R (ArXiv'2021)

Text Spotting

[x] ABCNet (CVPR'2020)
[x] ABCNetV2 (TPAMI'2021)
[x] SPTS (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:

@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.

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! 👬

mmocr dependencies

imgaug lmdb matplotlib numpy opencv-python pyclipper pycocotools rapidfuzz scikit-image

FAQs

What is mmocr?

OpenMMLab Text Detection, OCR, and NLP Toolbox. Visit Snyk Advisor to see a full health score report for mmocr, including popularity, security, maintenance & community analysis.

Is mmocr popular?

The python package mmocr receives a total of 1,891 weekly downloads. As such, mmocr popularity was classified as a recognized. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is mmocr well maintained?

We found indications that mmocr is an Inactive project. See the full package health analysis to learn more about the package maintenance status.

Is mmocr safe to use?

While scanning the latest version of mmocr, we found that a security review is needed. A total of 0 vulnerabilities or license issues were detected. See the full security scan results.

Last updated on 24 December-2024, at 06:01 (UTC).

Build a secure application checklist

Select a recommended open source package

Minimize your risk by selecting secure & well maintained open source packages

DONE

Scan your app for vulnerabilities

Scan your application to find vulnerabilities in your: source code, open source dependencies, containers and configuration files

SCAN NOW

Fix identified vulnerabilities

Easily fix your code by leveraging automatically generated PRs

AUTO FIX

Monitor for new issues

New vulnerabilities are discovered every day. Get notified if your application is affected

MONITOR APP

Package Health Score

Explore Similar Packages

Direct Usage Popularity

Security and license risk for latest version

Commit Frequency

Embed Package Health Score Badge

Keep your project healthy

Check your requirements.txt

Snyk Vulnerability Scanner

Readme

Latest Updates

Introduction

Major Features

Installation

Get Started

Model Zoo

Projects

Contributing

Acknowledgement

Citation

License

OpenMMLab Family

Welcome to the OpenMMLab community

mmocr dependencies

FAQs

What is mmocr?

Is mmocr popular?

Is mmocr well maintained?

Is mmocr safe to use?

Build a secure application checklist

Select a recommended open source package

Scan your app for vulnerabilities

Source Code

Open Source

Container

Config

Fix identified vulnerabilities

Monitor for new issues