Fusion of n-Tuple Based Classifiers for High Performance Handwritten Character Recognition

  • Konstantinos Sirlantzis
  • Sanaul Hoque
  • Michael C. Fairhurst
  • Ahmad Fuad Rezaur Rahman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2396)


In this paper we propose a novel system for handwritten character recognition which exploits the representational power of n-tuple based classifiers while addressing successfully the issues of extensive memory size requirements usually associated with them. To achieve this we develop a scheme based on the ideas of multiple classifier fusion in which the constituent classifiers are simplified versions of the highly successful scanning n-tuple classifier. In order to explore the behaviour and statistical properties of our architecture we perform a series of cross-validation experiments drawn from the field of handwritten character recognition. The paper concludes with a number of comparisons with results on the same data set achieved by a diverse set of classifiers. Our findings clearly demonstrate the significant gains that can be obtained, simultaneously in performance and memory space reduction, by the proposed system.


Character Recognition Recognition Error Chain Code Handwritten Character Constituent Member 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Konstantinos Sirlantzis
    • 1
  • Sanaul Hoque
    • 1
  • Michael C. Fairhurst
    • 1
  • Ahmad Fuad Rezaur Rahman
    • 2
  1. 1.Department of ElectronicsUniversity of Kent CanterburyKentUK
  2. 2.BCL Technologies Inc.Santa ClaraUSA

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