Metadata-Version: 2.1
Name: mmocr
Version: 0.2.0
Summary: OpenMMLab Text Detection, OCR, and NLP Toolbox
Home-page: https://github.com/open-mmlab/mmocr
Maintainer: MMOCR Authors
Maintainer-email: openmmlab@gmail.com
License: Apache License 2.0
Description: <div align="center">
          <img src="resources/mmocr-logo.png" width="500px"/>
        </div>
        
        ## Introduction
        
        English | [简体中文](README_zh-CN.md)
        
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        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](https://openmmlab.com/) project.
        
        The main branch works with **PyTorch 1.5+**.
        
        Documentation: https://mmocr.readthedocs.io/en/latest/.
        
        <div align="left">
          <img src="resources/illustration.jpg"/>
        </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 [getting_started.md](docs/getting_started.md) 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.
        
        ## [Model Zoo](https://mmocr.readthedocs.io/en/latest/modelzoo.html)
        
        Supported algorithms:
        
        <details open>
        <summary>Text Detection</summary>
        
        - [x] [DBNet](configs/textdet/dbnet/README.md) (AAAI'2020)
        - [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)
        
        </details>
        
        <details open>
        <summary>Text Recognition</summary>
        
        - [x] [CRNN](configs/textrecog/crnn/crnn_academic_dataset.py) (TPAMI'2016)
        - [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] [SegOCR](configs/bottom_up/higherhrnet/README.md) (Manuscript'2021)
        
        </details>
        
        <details open>
        <summary>Key Information Extraction</summary>
        
        - [x] [SDMG-R](configs/kie/sdmgr/README.md) (ArXiv'2021)
        
        </details>
        
        <details open>
        <summary>Named Entity Recognition</summary>
        
        - [x] [Bert-Softmax](configs/ner/bert_softmax/README.md) (NAACL'2019)
        
        </details>
        
        Please refer to [model_zoo](https://mmocr.readthedocs.io/en/latest/modelzoo.html) for more details.
        
        ## License
        
        This project is released under the [Apache 2.0 license](LICENSE).
        
        ## Citation
        
        If you find this project useful in your research, please consider cite:
        
        ```bibtex
        @misc{mmocr2021,
            title={MMOCR:  A Comprehensive Toolbox for Text Detection, Recognition and Understanding},
            author={MMOCR Contributors},
            howpublished = {\url{https://github.com/open-mmlab/mmocr}},
            year={2021}
        }
        ```
        
        ## Changelog
        
        v0.2.0 was released in 2021-5-18.
        
        
        ## Installation
        
        Please refer to [install.md](docs/install.md) for installation.
        
        ## Get Started
        
        Please see [getting_started.md](docs/getting_started.md) for the basic usage of MMOCR.
        
        ## Contributing
        
        We appreciate all contributions to improve MMOCR. Please refer to [CONTRIBUTING.md](.github/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.
        
        ## Projects in OpenMMLab
        
        - [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
        - [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
        - [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
        - [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
        - [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
        - [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
        - [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab's pose estimation toolbox and benchmark.
        - [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
        - [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image editing toolbox and benchmark.
        - [MMOCR](https://github.com/open-mmlab/mmocr): A Comprehensive Toolbox for Text Detection, Recognition and Understanding.
        - [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
        
Keywords: Text Detection,OCR,KIE,NLP
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
Provides-Extra: all
Provides-Extra: tests
Provides-Extra: build
Provides-Extra: optional
