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SAR

Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition

Abstract

Recognizing irregular text in natural scene images is challenging due to the large variance in text appearance, such as curvature, orientation and distortion. Most existing approaches rely heavily on sophisticated model designs and/or extra fine-grained annotations, which, to some extent, increase the difficulty in algorithm implementation and data collection. In this work, we propose an easy-to-implement strong baseline for irregular scene text recognition, using off-the-shelf neural network components and only word-level annotations. It is composed of a 31-layer ResNet, an LSTM-based encoder-decoder framework and a 2-dimensional attention module. Despite its simplicity, the proposed method is robust and achieves state-of-the-art performance on both regular and irregular scene text recognition benchmarks.

Dataset

Train Dataset

trainset instance_num repeat_num source
icdar_2011 3567 20 real
icdar_2013 848 20 real
icdar2015 4468 20 real
coco_text 42142 20 real
IIIT5K 2000 20 real
SynthText 2400000 1 synth
SynthAdd 1216889 1 synth, 1.6m in [1]
Syn90k 2400000 1 synth

Test Dataset

testset instance_num type
IIIT5K 3000 regular
SVT 647 regular
IC13 1015 regular
IC15 2077 irregular
SVTP 645 irregular, 639 in [1]
CT80 288 irregular

Results and Models

Methods Backbone Decoder Regular Text Irregular Text download
IIIT5K SVT IC13-1015 IC15-2077 SVTP CT80
SAR R31-1/8-1/4 ParallelSARDecoder 0.9533 0.8964 0.9369 0.7602 0.8326 0.9062 model | log
SAR-TTA R31-1/8-1/4 ParallelSARDecoder 0.9510 0.8964 0.9340 0.7862 0.8372 0.9132
SAR R31-1/8-1/4 SequentialSARDecoder 0.9553 0.9073 0.9409 0.7761 0.8093 0.8958 model | log
SAR-TTA R31-1/8-1/4 SequentialSARDecoder 0.9530 0.9073 0.9389 0.8002 0.8124 0.9028

Citation

@inproceedings{li2019show,
  title={Show, attend and read: A simple and strong baseline for irregular text recognition},
  author={Li, Hui and Wang, Peng and Shen, Chunhua and Zhang, Guyu},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={33},
  number={01},
  pages={8610--8617},
  year={2019}
}