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Scene Text Detection on Images Using Cellular Automata

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Cellular Automata (ACRI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7495))

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Abstract

Textual information in images constitutes a very rich source of high-level semantics for retrieval and indexing. In this paper, a new approach is proposed using Cellular Automata (CA) which strives towards identifying scene text on natural images. Initially, a binary edge map is calculated. Then, taking advantage of the CA flexibility, the transition rules are changing and are applied in four consecutive steps resulting in four time steps CA evolution. Finally, a post-processing technique based on edge projection analysis is employed for high density edge images concerning the elimination of possible false positives. Evaluation results indicate considerable performance gains without sacrificing text detection accuracy.

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Author information

Authors and Affiliations

  1. Image Processing and Multimedia Lab, Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, Greece

    Konstantinos Zagoris & Ioannis Pratikakis

Authors
  1. Konstantinos Zagoris
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  2. Ioannis Pratikakis
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, Laboratory of Electronics, Democritus University of Thrace, GR 67100, Xanthi, Greece

    Georgios Ch. Sirakoulis

  2. CSAI - Complex Systems and Artificial Intelligence Research Center, University of Milano-Bicocca, 20134, Milano, Italy

    Stefania Bandini

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© 2012 Springer-Verlag Berlin Heidelberg

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Zagoris, K., Pratikakis, I. (2012). Scene Text Detection on Images Using Cellular Automata. In: Sirakoulis, G.C., Bandini, S. (eds) Cellular Automata. ACRI 2012. Lecture Notes in Computer Science, vol 7495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33350-7_53

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  • DOI: https://doi.org/10.1007/978-3-642-33350-7_53

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33349-1

  • Online ISBN: 978-3-642-33350-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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