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Recognition of Car License Plate by Using Dynamical Thresholding Method and Enhanced Neural Networks

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Computer Analysis of Images and Patterns (CAIP 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2756))

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Abstract

In this paper, for the implementation of the recognition system of car license plates, the region extraction algorithm based on the contour tracking and the new enhanced neural networks learning algorithm are proposed, which extracts the areas of car license plate and the character areas from the car images and recognizes the car license numbers from the extracted areas. And a candidate area was selected, whose density rate was corresponding to the properties of the car license plate obtained in the condition of the car license plate. The contour tracking algorithm extracted the feature areas covering the areas of characters from the car license plate. As well, the enhanced neural networks learning algorithm, combining the modified ART1 and supervised learning algorithm, recognized the car license numbers from the feature areas.

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Author information

Authors and Affiliations

  1. Dept. of Computer Engineering, Silla University, Korea

    Kwang-Baek Kim

  2. Research Center for Electronic Ceramics(RCEC) of Dongeui University, Korea

    Si-Woong Jang

  3. Dept. of Computer Engineering, Miryang National University, Korea

    Cheol-Ki Kim

Authors
  1. Kwang-Baek Kim
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  2. Si-Woong Jang
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  3. Cheol-Ki Kim
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Editor information

Editors and Affiliations

  1. Institute of Mathematics and Computing Science, University of Groningen, P.O.Box 800, 9700 AV, Groningen, The Netherlands

    Nicolai Petkov

  2. Institute of Mathematics and Computing Science, University of Groningen, Blauwborgje 3, 9747 AC, Groningen, The Netherlands

    Michel A. Westenberg

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© 2003 Springer-Verlag Berlin Heidelberg

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Kim, KB., Jang, SW., Kim, CK. (2003). Recognition of Car License Plate by Using Dynamical Thresholding Method and Enhanced Neural Networks. In: Petkov, N., Westenberg, M.A. (eds) Computer Analysis of Images and Patterns. CAIP 2003. Lecture Notes in Computer Science, vol 2756. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45179-2_39

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  • DOI: https://doi.org/10.1007/978-3-540-45179-2_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40730-0

  • Online ISBN: 978-3-540-45179-2

  • eBook Packages: Springer Book Archive

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