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# MASTER
> [MASTER: Multi-aspect non-local network for scene text recognition](https://arxiv.org/abs/1910.02562)
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## Abstract
Attention-based scene text recognizers have gained huge success, which leverages a more compact intermediate representation to learn 1d- or 2d- attention by a RNN-based encoder-decoder architecture. However, such methods suffer from attention-drift problem because high similarity among encoded features leads to attention confusion under the RNN-based local attention mechanism. Moreover, RNN-based methods have low efficiency due to poor parallelization. To overcome these problems, we propose the MASTER, 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 great training efficiency because of high training parallelization and a high-speed inference because of an efficient memory-cache mechanism. Extensive experiments on various benchmarks demonstrate the superior performance of our MASTER on both regular and irregular scene text.
<div align=center>
<img src="https://user-images.githubusercontent.com/65173622/164642001-037f81b7-37dd-4808-a6a9-09ff6f6a17ea.JPG">
</div>
## Dataset
### Train Dataset
| trainset | instance_num | repeat_num | source |
| :-------: | :----------: | :--------: | :----: |
| SynthText | 7266686 | 1 | synth |
| SynthAdd | 1216889 | 1 | synth |
| Syn90k | 8919273 | 1 | synth |
### Test Dataset
| testset | instance_num | type |
| :-----: | :----------: | :-------: |
| IIIT5K | 3000 | regular |
| SVT | 647 | regular |
| IC13 | 1015 | regular |
| IC15 | 2077 | irregular |
| SVTP | 645 | irregular |
| CT80 | 288 | irregular |
## Results and Models
| Methods | Backbone | | Regular Text | | | | Irregular Text | | download |
| :------------------------------------------------------------: | :-----------: | :----: | :----------: | :---: | :-: | :---: | :------------: | :---: | :-------------------------------------------------------------------------: |
| | | IIIT5K | SVT | IC13 | | IC15 | SVTP | CT80 | |
| [MASTER](/configs/textrecog/master/master_r31_12e_ST_MJ_SA.py) | R31-GCAModule | 95.27 | 89.8 | 95.17 | | 77.03 | 82.95 | 89.93 | [model](https://download.openmmlab.com/mmocr/textrecog/master/master_r31_12e_ST_MJ_SA-787edd36.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/master/master_r31_12e_ST_MJ_SA-787edd36.log.json) |
## Citation
```bibtex
@article{Lu2021MASTER,
title={{MASTER}: Multi-Aspect Non-local Network for Scene Text Recognition},
author={Ning Lu and Wenwen Yu and Xianbiao Qi and Yihao Chen and Ping Gong and Rong Xiao and Xiang Bai},
journal={Pattern Recognition},
year={2021}
}
```
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