MMOCR
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/.
Major Features
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Comprehensive Pipeline
The toolbox supports not only text detection and text recognition, but also their downstream tasks such as key information extraction.
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Multiple Models
The toolbox supports a wide variety of state-of-the-art models for text detection, text recognition and key information extraction.
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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 for how to construct a customized model.
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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
Supported algorithms:
Text Detection
Text Recognition
- [x] CRNN (TPAMI'2016)
- [x] NRTR (ICDAR'2019)
- [x] RobustScanner (ECCV'2020)
- [x] SAR (AAAI'2019)
- [x] SegOCR (Manuscript'2021)
Key Information Extraction
- [x] SDMG-R (ArXiv'2021)
Named Entity Recognition
- [x] Bert-Softmax (NAACL'2019)
Please refer to model_zoo for more details.