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Training Set Expansion in Handwritten Character Recognition

  • Javier Cano
  • Juan-Carlos Perez-Cortes
  • Joaquim Arlandis
  • Rafael Llobet
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2396)

Abstract

In this paper, a process of expansion of the training set by synthetic generation of handwritten uppercase letters via deformations of natural images is tested in combination with an approximate k-Nearest Neighbor (k-NN) classifier. It has been previously shown [11] [10] that approximate nearest neighbors search in large databases can be successfully used in an OCR task, and that significant performance improvements can be consistently obtained by simply increasing the size of the training set. In this work, extensive experiments adding distorted characters to the training set are performed, and the results are compared to directly adding new natural samples to the set of prototypes.

Keywords

Synthetic Image Local Database Neighbor Rule Handwritten Character Neighbor Search Algorithm 
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

  • Javier Cano
    • 1
  • Juan-Carlos Perez-Cortes
    • 1
  • Joaquim Arlandis
    • 1
  • Rafael Llobet
    • 1
  1. 1.Instituto Tecnologico de InformaticaUniversidad Politecnica de ValenciaValenciaSpain

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