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Link to original content: https://doi.org/10.1007/978-3-319-05167-3_6
Sign Detection Based Text Localization in Mobile Device Captured Scene Images | SpringerLink
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Sign Detection Based Text Localization in Mobile Device Captured Scene Images

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Camera-Based Document Analysis and Recognition (CBDAR 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8357))

Abstract

Sign text is one of the most seen text types appearing in scene images. In this paper, we present a new sign text localization method for scene images captured by mobile device. The candidate characters are first localized by detecting closed boundaries in the image. Then, based on the properties of signboard, the convex regions that contain enough candidate characters are extracted and marked as sign regions. After removing the false positives using the proposed layer analysis, the candidate characters inside the detected sign regions are yielded as sign text. A sign text database with 241 images captured by a mobile device was used to evaluate our method. The experimental results demonstrate the validity of the proposed method.

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Acknowledgment

The authors wish to thank K.L. Bouman, G. Abdollahian, M. Boutin, and E.J. Delp at Purdue University for proving us the database used in the paper.

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Authors and Affiliations

  1. Computer Science and Engineering Department, University of South Florida, Tampa, FL, 33620, USA

    Jing Zhang & Rangachar Kasturi

Authors
  1. Jing Zhang
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  2. Rangachar Kasturi
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Corresponding author

Correspondence to Jing Zhang .

Editor information

Editors and Affiliations

  1. Graudate School of Engineering, Osaka Prefecture University, Osaka, Japan

    Masakazu Iwamura

  2. The University of Western Australia, Crawley, West Australia, Australia

    Faisal Shafait

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Zhang, J., Kasturi, R. (2014). Sign Detection Based Text Localization in Mobile Device Captured Scene Images. In: Iwamura, M., Shafait, F. (eds) Camera-Based Document Analysis and Recognition. CBDAR 2013. Lecture Notes in Computer Science(), vol 8357. Springer, Cham. https://doi.org/10.1007/978-3-319-05167-3_6

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  • DOI: https://doi.org/10.1007/978-3-319-05167-3_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05166-6

  • Online ISBN: 978-3-319-05167-3

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