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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.

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}
}

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 IC15 SVTP CT80
SAR R31-1/8-1/4 ParallelSARDecoder 95.0 89.6 93.7 79.0 82.2 88.9 model | log
SAR R31-1/8-1/4 SequentialSARDecoder 95.2 88.7 92.4 78.2 81.9 89.6 model | log

Chinese Dataset

Results and Models

Methods Backbone Decoder download
SAR R31-1/8-1/4 ParallelSARDecoder model | log | dict

Notes:

  • R31-1/8-1/4 means the height of feature from backbone is 1/8 of input image, where 1/4 for width.
  • We did not use beam search during decoding.
  • We implemented two kinds of decoder. Namely, ParallelSARDecoder and SequentialSARDecoder.
    • ParallelSARDecoder: Parallel decoding during training with LSTM layer. It would be faster.
    • SequentialSARDecoder: Sequential Decoding during training with LSTMCell. It would be easier to understand.
  • For train dataset.
    • We did not construct distinct data groups (20 groups in [1]) to train the model group-by-group since it would render model training too complicated.
    • Instead, we randomly selected 2.4m patches from Syn90k, 2.4m from SynthText and 1.2m from SynthAdd, and grouped all data together. See config for details.
  • We used 48 GPUs with total_batch_size = 64 * 48 in the experiment above to speedup training, while keeping the initial lr = 1e-3 unchanged.

References

[1] Li, Hui and Wang, Peng and Shen, Chunhua and Zhang, Guyu. Show, attend and read: A simple and strong baseline for irregular text recognition. In AAAI 2019.