Feature Maps for Non-supervised Classification of Low-Uniform Patterns of Handwritten Letters

  • Pilar Gómez-Gil
  • Guillermo de-los-Santos-Torres
  • Manuel Ramírez-Cortés
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3287)

Abstract

When input data is noisy and with a lack of uniformity, classification is a very difficult problem, because decision regions are hard to define in an optimal way. This is the case of recognition of old handwritten manuscript characters, where patterns of the same class may be very different from each other, and patterns of different classes may be similar in terms of Euclidian distances between their feature vectors. In this paper we present the results obtained when a non-supervised method is used to create feature maps of possible classes in handwriting letters. The prototypes generated in the map present a topological relationship; therefore similar prototypes are near each other. This organization helps to solve the problem of variance in the patterns, allowing a better classification when compared with other supervised classification method, a nearest-neighbor algorithm. The feature map was built using a Self-organized Feature Map (SOFM) neural network.

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References

  1. 1.
    Haykin, S.: Neural Networks: a Comprehensive Foundation. Macmillan College Publishing Company, New York (1994)MATHGoogle Scholar
  2. 2.
    Kohonen, T.: Self-Organized formation of topologically correct feature maps. Biological Cybernetics 43, 59–69 (1982)MATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Tao, J.T., Gonzalez, R.C.: Pattern Recognition Principles. Addison-Wesley, Reading (1974)Google Scholar
  4. 4.
    De-los-Santos-Torres, G.: Reconocedor de Caracteres Manuscritos. Master thesis. Departamento de Ingeniería en Sistemas Computacionales. Universidad de las Américas, Puebla (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Pilar Gómez-Gil
    • 1
  • Guillermo de-los-Santos-Torres
    • 1
  • Manuel Ramírez-Cortés
    • 1
  1. 1.Department of Computer Science and CENTIAUniversidad de las Américas, PueblaSanta Catarina M. CholulaMéxico

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