# 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      | IC13-1015 |     | IC15-2077 |      SVTP      |  CT80  |                                                                   |
| [MASTER](/configs/textrecog/master/master_resnet31_12e_st_mj_sa.py) | R31-GCAModule | 0.9490 |    0.8887    |  0.9517   |     |  0.7650   |     0.8465     | 0.8889 | [model](https://download.openmmlab.com/mmocr/textrecog/master/master_resnet31_12e_st_mj_sa/master_resnet31_12e_st_mj_sa_20220915_152443-f4a5cabc.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/master/master_resnet31_12e_st_mj_sa/20220915_152443.log) |
| [MASTER-TTA](/configs/textrecog/master/master_resnet31_12e_st_mj_sa.py) | R31-GCAModule | 0.9450 |    0.8887    |  0.9478   |     |  0.7906   |     0.8481     | 0.8958 |                                                                   |

## 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}
}
```