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Scene Text Detection Based on Robust Stroke Width Transform and Deep Belief Network

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Computer Vision -- ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9004))

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Abstract

Text detection in natural scene images is an open and challenging problem due to the significant variations of the appearance of the text itself and its interaction with the context. In this paper, we present a novel text detection method combining two main ingredients: the robust extension of Stroke Width Transform (SWT) and the Deep Belief Network (DBN) based discrimination of text objects from other scene components. In the former, smoothness-based edge information is combined with gradient for generating high quality edge images, and various edge cues are exploited in Connected Component (CC) analysis on basis of SWT to eliminate inter-character and intra-character errors. In the latter, DBN is exploited for learning efficient representations discriminating character and non-character CCs, resulting in the improved detection accuracy. The proposed method is evaluated on ICDAR and SVT public datasets and achieves the state-of-the-art results, which reveal the effectiveness of the method.

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Notes

  1. 1.

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Acknowledgement

Research supported by the National Science Foundation of China under Grant Nos. 61003113, 61272218 and 61321491.

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Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China

    Hailiang Xu, Like Xue & Feng Su

Authors
  1. Hailiang Xu
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  2. Like Xue
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  3. Feng Su
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Corresponding author

Correspondence to Feng Su .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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Xu, H., Xue, L., Su, F. (2015). Scene Text Detection Based on Robust Stroke Width Transform and Deep Belief Network. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9004. Springer, Cham. https://doi.org/10.1007/978-3-319-16808-1_14

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  • DOI: https://doi.org/10.1007/978-3-319-16808-1_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16807-4

  • Online ISBN: 978-3-319-16808-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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