MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding
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Abstract (arXiv)
We present MMOCR-an open-source toolbox which provides a comprehensive pipeline for text detection and recognition, as well as their downstream tasks such as named entity recognition and key information extraction. MMOCR implements 14 state-of-the-art algorithms, which is significantly more than all the existing open-source OCR projects we are aware of to date. To facilitate future research and industrial applications of text recognition-related problems, we also provide a large number of trained models and detailed benchmarks to give insights into the performance of text detection, recognition and understanding. MMOCR is publicly released at https://github.com/open-mmlab/mmocr.
Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). Builds on 42 of its 47 references that are in the map. First posted on arXiv 14 Aug 2021.
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42 of 47 references.
References · 42
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- 2017EAST: An Efficient and Accurate Scene Text Detectorcited by 248
- 2019Real-time Scene Text Detection with Differentiable Binarizationcited by 212
- 2015Named Entity Recognition with Bidirectional LSTM-CNNscited by 201
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- 2020PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networkscited by 70
- 2019Rosetta: Large scale system for text detection and recognition in imagescited by 69
- 2018Scene Text Recognition from Two-Dimensional Perspectivecited by 57
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- 2019Symmetry-constrained Rectification Network for Scene Text Recognitioncited by 45
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- 2019Aggregation Cross-Entropy for Sequence Recognitioncited by 22
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Citing papers per year: 2021: 2; 2022: 7; 2023: 8; 2024: 14; 2025: 8; 2026: 1.
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