README.md

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}
}