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Code for the paper "MASTER: Multi-Aspect Non-local Network for Scene Text Recognition" (Pattern Recognition 2021)
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| 2026-07-24 | 281 |
| 2026-07-25 | 281 |
| 2026-07-28 | 281 |
| 2026-07-30 | 281 |
| 2026-08-06 | 281 |
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# MASTER-PyTorch  <!--   --> <div align=center> <img src="https://github.com/wenwenyu/MASTER-pytorch/blob/main/assets/logo.jpeg" width="200" height="200" /> </div> PyTorch reimplementation of ["MASTER: Multi-Aspect Non-local Network for Scene Text Recognition"](https://arxiv.org/abs/1910.02562) (Pattern Recognition 2021). This project is different from our original implementation that builds on the privacy codebase FastOCR of the company. You can also find Tensorflow reimplementation at [MASTER-TF](https://github.com/jiangxiluning/MASTER-TF) repository, and the performance is almost identical. (PS. Logo inspired by the Master Oogway in Kung Fu Panda) ## News * 2021/07: [MASTER-mmocr](https://github.com/JiaquanYe/MASTER-mmocr), reimplementation of MASTER by mmocr. [@Jiaquan Ye](https://github.com/JiaquanYe) * 2021/07: [TableMASTER-mmocr](https://github.com/JiaquanYe/TableMASTER-mmocr), 2nd solution of ICDAR 2021 Competition on Scientific Literature Parsing Task B based on MASTER. [@Jiaquan Ye](https://github.com/JiaquanYe) * 2021/07: Talk can be found at [here](https://www.bilibili.com/video/BV1T44y1m7vc) (Chinese). * 2021/05: [Savior](https://github.com/novioleo/Savior), which aims to provide a simple, lightweight, fast integrated, pipelined deployment framework for RPA, is now integrated MASTER for captcha recognition. [@Tao Luo](https://github.com/novioleo) * 2021/04: Slides can be found at [here](https://github.com/wenwenyu/MASTER-pytorch/blob/main/assets/MASTER.pdf). ## Honors based on MASTER * 1st place (2021/05) solution to [ICDAR 2021 Competition on Scientific Table Image Recognition to LaTeX (Subtask I: Table structure reconstruction)](https://competitions.codalab.org/competitions/26979) * 1st place (2021/05) solution to [ICDAR 2021 Competition on Scientific Table Image Recognition to LaTeX (Subtask II: Table content reconstruction)](https://competitions.codalab.org/competitions/26979) * 2nd place (2021/05) solution to [ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table recognition](https://icdar2021.org/program-2/competitions/competition-on-scientific-literature-parsing/) * 1st place (2020/10) solution to [ICDAR 2019 Robust Reading Challenge on Reading Chinese Text on Signboard (task2)](https://rrc.cvc.uab.es/?ch=12&com=evaluation&task=2) * 2nd and 5th places (2020/10) in [The 5th China Innovation Challenge on Handwritten Mathematical Expression Recognition](https://www.heywhale.com/home/competition/5f703ac023f41e002c3ed5e4/content/6) * 4th place (2019/08) of [ICDAR 2017 Robust Reading Challenge on COCO-Text (task2)](https://rrc.cvc.uab.es/?ch=5&com=evaluation&task=2) * More will be released ## Contents * [Introduction](#introduction) * [Requirements](#requirements) * [Usage](#usage) * [Prepare Datasets](#prepare-datasets) * [Distributed training with config files](#distributed-training-with-config-files) * [Using Multiple Node](#using-multiple-node) * [Debug mode on one GPU/CPU training with config files](#debug-mode-on-one-gpucpu-training-with-config-files) * [Resuming from checkpoints](#resuming-from-checkpoints) * [Finetune from checkpoints](#finetune-from-checkpoints) * [Testing from checkpoints](#testing-from-checkpoints) * [Evaluation](#evaluation) * [Customization](#customization) * [Checkpoints](#checkpoints) * [Tensorboard Visualization](#tensorboard-visualization) * [TODO](#todo) * [Citations](#citations) * [License](#license) * [Acknowledgements](#acknowledgements) ## Introduction MASTER is a self-attention based scene text recognizer that (1) not only encodes the input-output attention, but also learns self-attention which encodes feature-feature and target-target relationships inside the encoder and decoder and (2) learns a more powerful and robust intermediate representation to spatial distortion and (3) owns a better t
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7a27f53f9f28a0a2, topic:ocr
matched fp:7a27f53f9f28a0a2, topic:transformer