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BorderNet: An Efficient Border-Attention Text Detector

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Computer Vision – ACCV 2022 (ACCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13847))

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Abstract

Recently, segmentation-based text detection methods are quite popular in the scene text detection field, because of their superiority for text instances with arbitrary shapes and extreme aspect ratios. However, the vast majority of the existing segmentation-based methods are difficult to detect curved and dense text instances due to principle of these methods. In this paper, we propose a novel text detection method named BorderNet. The key idea of BorderNet is making full use of border-center information to detect the curve and dense text. Furthermore, a efficient Multi-Scale Feature Enhancement Module is proposed to improve the scale and shape robustness by enhancing features of different scales adaptively. Our method outperforms SOTA on multiple datasets, achieving 89% accuracy on ICDAR2015 and 87.1% accuracy on Total-Text. What’s more, we can maintain 84.5% accuracy on DAST1500.

J. Cheng and L. Xie—Authors contribute equally.

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Authors and Affiliations

  1. AI R&D Department, Kingsoft Office, Beijing, China

    Juntao Cheng, Liangru Xie & Cheng Du

Authors
  1. Juntao Cheng
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  2. Liangru Xie
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  3. Cheng Du
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Corresponding author

Correspondence to Juntao Cheng .

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Editors and Affiliations

  1. University of Wollongong, Wollongong, NSW, Australia

    Lei Wang

  2. University of Bonn, Bonn, Germany

    Juergen Gall

  3. University of Adelaide, Adelaide, SA, Australia

    Tat-Jun Chin

  4. National Institute of Informatics, Tokyo, Japan

    Imari Sato

  5. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

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Cheng, J., Xie, L., Du, C. (2023). BorderNet: An Efficient Border-Attention Text Detector. In: Wang, L., Gall, J., Chin, TJ., Sato, I., Chellappa, R. (eds) Computer Vision – ACCV 2022. ACCV 2022. Lecture Notes in Computer Science, vol 13847. Springer, Cham. https://doi.org/10.1007/978-3-031-26293-7_36

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  • DOI: https://doi.org/10.1007/978-3-031-26293-7_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26292-0

  • Online ISBN: 978-3-031-26293-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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