MASTER
MASTER: Multi-aspect non-local network for scene text recognition
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.
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 |
R31-GCAModule |
0.9490 |
0.8887 |
0.9517 |
|
0.7650 |
0.8465 |
0.8889 |
model | log |
| MASTER-TTA |
R31-GCAModule |
0.9450 |
0.8887 |
0.9478 |
|
0.7906 |
0.8481 |
0.8958 |
|
Citation
@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}
}
