Package: mmocr
OpenMMLab websiteHOTOpenMMLab platformTRY IT OUT📘Documentation|🛠️Installation|👀Model Zoo|🆕Update News|🤔Reporting IssuesEnglish |简体中文Latest UpdatesThe default branch is nowmainand the code on the branch has been upgraded to v1.0.0. The oldmainbranch (v0.6.3) code now exists on the0.xbranch.If you have been using themainbranch and encounter upgrade issues, please read theMigration Guideand notes onBranches.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 PreparerUpdated FAQ and documentationDeprecation of file_client_args in favor of backend_argsAdded a new MMOCR tutorial notebookTo know more about the updates in MMOCR 1.0, please refer toWhat's New in MMOCR 1.x, or
ReadChangelogfor more details!IntroductionMMOCR 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 theOpenMMLabproject.The main branch works withPyTorch 1.6+.Major FeaturesComprehensive PipelineThe toolbox supports not only text detection and text recognition, but also their downstream tasks such as key information extraction.Multiple ModelsThe toolbox supports a wide variety of state-of-the-art models for text detection, text recognition and key information extraction.Modular DesignThe 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 toOverviewfor how to construct a customized model.Numerous UtilitiesThe 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.InstallationMMOCR depends onPyTorch,MMEngine,MMCVandMMDetection.
Below are quick steps for installation.
Please refer toInstall Guidefor more detailed instruction.condacreate-nopen-mmlabpython=3.8pytorch=1.10cudatoolkit=11.3torchvision-cpytorch-y
condaactivateopen-mmlab
pip3installopenmim
gitclonehttps://github.com/open-mmlab/mmocr.gitcdmmocr
miminstall-e.Get StartedPlease seeQuick Runfor the basic usage of MMOCR.Model ZooSupported algorithms:BackBoneoCLIP(ECCV'2022)Text DetectionDBNet(AAAI'2020) /DBNet++(TPAMI'2022)Mask R-CNN(ICCV'2017)PANet(ICCV'2019)PSENet(CVPR'2019)TextSnake(ECCV'2018)DRRG(CVPR'2020)FCENet(CVPR'2021)Text RecognitionABINet(CVPR'2021)ASTER(TPAMI'2018)CRNN(TPAMI'2016)MASTER(PR'2021)NRTR(ICDAR'2019)RobustScanner(ECCV'2020)SAR(AAAI'2019)SATRN(CVPR'2020 Workshop on Text and Documents in the Deep Learning Era)SVTR(IJCAI'2022)Key Information ExtractionSDMG-R(ArXiv'2021)Text SpottingABCNet(CVPR'2020)ABCNetV2(TPAMI'2021)SPTS(ACM MM'2022)Please refer tomodel_zoofor more details.ProjectsHereare 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.ContributingWe appreciate all contributions to improve MMOCR. Please refer toCONTRIBUTING.mdfor the contributing guidelines.AcknowledgementMMOCR 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.CitationIf 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}}LicenseThis project is released under theApache 2.0 license.OpenMMLab FamilyMMEngine: OpenMMLab foundational library for training deep learning modelsMMCV: 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 communityScan the QR code below to follow the OpenMMLab team'sZhihu Official Accountand join the OpenMMLab team'sQQ Group, or join the official communication WeChat group by adding the WeChat, or join ourSlackWe 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
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