Statistical neural network based classifiers for letter recognition


ERKMEN B., YILDIRIM T.

INTELLIGENT COMPUTING IN SIGNAL PROCESSING AND PATTERN RECOGNITION, vol.345, pp.1081-1086, 2006 (Journal Indexed in SCI) identifier

  • Publication Type: Article / Article
  • Volume: 345
  • Publication Date: 2006
  • Doi Number: 10.1007/978-3-540-37258-5_140
  • Title of Journal : INTELLIGENT COMPUTING IN SIGNAL PROCESSING AND PATTERN RECOGNITION
  • Page Numbers: pp.1081-1086

Abstract

In this paper, Statistical Neural Networks have been proven to be an effective classifier method for large sample and high dimensional letter recognition problem. For this purpose, Probabilistic Neural Network (PNN) and General Regression Neural Networks (GRNN) have been applied to classify the 26 capital letters in the English alphabet. Principal Component Analysis (PCA) has been established as a feature extraction and a data compression method to achieve less computational complexity. The low computational complexity obtained by PCA provides a solution for high dimensional letter recognition problem for online operations. Simulation results illustrate that GRNN and PNN are suitable and effective methods for solving classification problems with higher classification accuracy and better generalization performances than their counterparts.