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ASTER

ASTER: An Attentional Scene Text Recognizer with Flexible Rectification

Abstract

A challenging aspect of scene text recognition is to handle text with distortions or irregular layout. In particular, perspective text and curved text are common in natural scenes and are difficult to recognize. In this work, we introduce ASTER, an end-to-end neural network model that comprises a rectification network and a recognition network. The rectification network adaptively transforms an input image into a new one, rectifying the text in it. It is powered by a flexible Thin-Plate Spline transformation which handles a variety of text irregularities and is trained without human annotations. The recognition network is an attentional sequence-to-sequence model that predicts a character sequence directly from the rectified image. The whole model is trained end to end, requiring only images and their groundtruth text. Through extensive experiments, we verify the effectiveness of the rectification and demonstrate the state-of-the-art recognition performance of ASTER. Furthermore, we demonstrate that ASTER is a powerful component in end-to-end recognition systems, for its ability to enhance the detector.

Dataset

Train Dataset

trainset instance_num repeat_num note
Syn90k 8919273 1 synth
SynthText 7239272 1 alphanumeric

Test Dataset

testset instance_num note
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
ASTER ResNet45 0.9357 0.8949 0.9281 0.7665 0.8062 0.8507 model | log
ASTER-TTA ResNet45 0.9337 0.8949 0.9251 0.7925 0.8109 0.8507

Citation

@article{shi2018aster,
  title={Aster: An attentional scene text recognizer with flexible rectification},
  author={Shi, Baoguang and Yang, Mingkun and Wang, Xinggang and Lyu, Pengyuan and Yao, Cong and Bai, Xiang},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={41},
  number={9},
  pages={2035--2048},
  year={2018},
  publisher={IEEE}
}