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PlugNet: Degradation Aware Scene Text Recognition Supervised by a Pluggable Super-Resolution Unit

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Computer Vision – ECCV 2020 (ECCV 2020)

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Abstract

In this paper, we address the problem of recognizing degradation images that are suffering from high blur or low-resolution. We propose a novel degradation aware scene text recognizer with a pluggable super-resolution unit (PlugNet) to recognize low-quality scene text to solve this task from the feature-level. The whole networks can be trained end-to-end with a pluggable super-resolution unit (PSU) and the PSU will be removed after training so that it brings no extra computation. The PSU aims to obtain a more robust feature representation for recognizing low-quality text images. Moreover, to further improve the feature quality, we introduce two types of feature enhancement strategies: Feature Squeeze Module (FSM) which aims to reduce the loss of spatial acuity and Feature Enhance Module (FEM) which combines the feature maps from low to high to provide diversity semantics. As a consequence, the PlugNet achieves state-of-the-art performance on various widely used text recognition benchmarks like IIIT5K, SVT, SVTP, ICDAR15 and etc.

Y. Mou and L. Tan — Equal Contribution.

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Acknowledgement

This work was supported by the Project of the National Natural Science Foundation of China Grant No. 61977027 and No. 61702208, the Hubei Province Technological Innovation Major Project Grant No. 2019AAA044 and the Colleges Basic Research and Operation of MOE Grant No. CCNU19Z02002, CCNU18KFY02.

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Authors and Affiliations

  1. AI -Labs, GuangZhou Image Data Technology Co., Ltd., Guangzhou, China

    Yongqiang Mou, Hui Yang, Rui Yan & Yaohong Huang

  2. Nercel, Central China Normal University, Wuhan, China

    Lei Tan, Jingying Chen & Leyuan Liu

Authors
  1. Yongqiang Mou
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  2. Lei Tan
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  3. Hui Yang
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  4. Jingying Chen
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  5. Leyuan Liu
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  7. Yaohong Huang
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Corresponding author

Correspondence to Yongqiang Mou .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Mou, Y. et al. (2020). PlugNet: Degradation Aware Scene Text Recognition Supervised by a Pluggable Super-Resolution Unit. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12360. Springer, Cham. https://doi.org/10.1007/978-3-030-58555-6_10

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  • DOI: https://doi.org/10.1007/978-3-030-58555-6_10

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  • Online ISBN: 978-3-030-58555-6

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