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SATRN

On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention

Abstract

Scene text recognition (STR) is the task of recognizing character sequences in natural scenes. While there have been great advances in STR methods, current methods still fail to recognize texts in arbitrary shapes, such as heavily curved or rotated texts, which are abundant in daily life (e.g. restaurant signs, product labels, company logos, etc). This paper introduces a novel architecture to recognizing texts of arbitrary shapes, named Self-Attention Text Recognition Network (SATRN), which is inspired by the Transformer. SATRN utilizes the self-attention mechanism to describe two-dimensional (2D) spatial dependencies of characters in a scene text image. Exploiting the full-graph propagation of self-attention, SATRN can recognize texts with arbitrary arrangements and large inter-character spacing. As a result, SATRN outperforms existing STR models by a large margin of 5.7 pp on average in "irregular text" benchmarks. We provide empirical analyses that illustrate the inner mechanisms and the extent to which the model is applicable (e.g. rotated and multi-line text). We will open-source the code.

Dataset

Train Dataset

trainset instance_num repeat_num source
SynthText 7266686 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 Regular Text Irregular Text download
IIIT5K SVT IC13 IC15 SVTP CT80
Satrn 96.1 93.5 95.7 84.1 88.5 90.3 model | log
Satrn_small 94.7 91.3 95.4 81.9 85.9 86.5 model | log

Citation

@article{junyeop2019recognizing,
  title={On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention},
  author={Junyeop Lee, Sungrae Park, Jeonghun Baek, Seong Joon Oh, Seonghyeon Kim, Hwalsuk Lee},
  year={2019}
}