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| # MASTER | |
| > [MASTER: Multi-aspect non-local network for scene text recognition](https://arxiv.org/abs/1910.02562) | |
| <!-- [ALGORITHM] --> | |
| ## 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} | |
| } | |
| ``` | |