1. Generate dataset as per icdar2015 format. You can refer sample dataset avilable inside dataset folder and script and put it inside data folder.

2. generate instances_training.json and instances_test.json with following command:

   python tools/data/textdet/icdar_converter.py /path/to/dataset -o /path/to/daatset -d icdar2015 --split-list training test
   

3. Modify config file as per requirements such as dataset foldername, epoch, learning rate etc..

4. Run command:

   python3 tools/train.py configs/textdet/textsnake/textsnake_r50_fpn_unet_1200e_ctw1500_custom.py --load-from "https://download.openmmlab.com/mmocr/textdet/textsnake/textsnake_r50_fpn_unet_1200e_ctw1500-27f65b64.pth" --work-dir "ckpt/finetuning-textsnake-v1"
   

   Note: For adding trained models of mmocr using load-from, you must give https link, for model trained by you, can specify local file path.

Make sure dataset is valid, wrong annotations such as coordinates order affect fine-tuning, if wrong coordinates order passed it will not detect any text in inference.

 

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

 

English | 简体中文

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+.

Documentation: https://mmocr.readthedocs.io/en/latest/.

• 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 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.

Supported algorithms:

Please refer to model_zoo for more details.

This project is released under the Apache 2.0 license.

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}
}

v0.4.1 was released in 2022-01-27.

Please refer to our Install Guide.

Please see Getting Started for the basic usage of MMOCR.

We appreciate all contributions to improve MMOCR. Please refer to CONTRIBUTING.md for the contributing guidelines.

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.

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• 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.
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• MMFlow: OpenMMLab optical flow toolbox and benchmark.
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• MMGeneration: OpenMMLab image and video generative models toolbox.
• MMDeploy: OpenMMLab model deployment framework.