A hierarchical classifier for multifont digits

  • C. Rodriguez
  • J. Muguerza
  • M. Navarro
  • A. Zárate
  • J. I. Mar'in
  • J. M. Pérez
Poster Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1451)


In this paper, the automatic recognition of broken and blurred, multifont typewritten digits in forms will be addressed. The classification, which is based on the utilization of a global feature, is divided in two phases: first, a minimum distance method (1-NN) is applied to provide a global classification of the patterns in a form; second, the patterns in the form previously classified are used to validate, or reject and reclassify them, on the basis of the mean distance to the predefined classes. In this way, a classification accuracy rate of 99.42% has been achieved.


Near Neighbor Optical Character Recognition Handwritten Digit Global Accuracy Hierarchical Classifier 
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 1998

Authors and Affiliations

  • C. Rodriguez
    • 1
  • J. Muguerza
    • 1
  • M. Navarro
    • 1
  • A. Zárate
    • 1
  • J. I. Mar'in
    • 1
  • J. M. Pérez
    • 1
  1. 1.Computer Architecture and Technology DepartmentThe Basque Country University (UPV/EHU)DonostiaSpain

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