Dynamic Zoning Selection for Handwritten Character Recognition

  • Luciane Y. Hirabara
  • Simone B. K. Aires
  • Cinthia O. A. Freitas
  • Alceu S. BrittoJr.
  • Robert Sabourin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7042)


This paper presents a two-level based character recognition method in which a dynamically selection of the most promising zoning scheme for feature extraction allows us to obtain interesting results for character recognition. The first level consists of a conventional neural network and a look-up-table that is used to suggest the best zoning scheme for a given unknown character. The information provided by the first level drives the second level in the selection of the appropriate feature extraction method and the corresponding class-modular neural network. The experimental protocol has shown significant recognition rates for handwritten characters (from 80.82% to 88.13%).


dynamic selection zoning mechanism handwritten character recognition 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Luciane Y. Hirabara
    • 1
  • Simone B. K. Aires
    • 1
  • Cinthia O. A. Freitas
    • 1
  • Alceu S. BrittoJr.
    • 1
  • Robert Sabourin
    • 2
  1. 1.PUCPR-Pontificia Universidade Católica do ParanáBrazil
  2. 2.ETS-Ecole de Technologie SupérieureCanada

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