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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}
}