Comparison of SVM and ANN performance for handwritten character classification


Kahraman F., Çapar A. , Ayvaci A., Demirel H., Gokmen M.

IEEE 12th Signal Processing and Communications Applications Conference, Kusadasi, Turkey, 28 - 30 April 2004, pp.615-618 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/siu.2004.1338604
  • City: Kusadasi
  • Country: Turkey
  • Page Numbers: pp.615-618

Abstract

This study is about the selection of classifiers in handwritten character recogntition. The aim of the study is to determine the most appropriate classifier type for a given handwritten character feature vector. PCA based features were classified by both Multilayer Artificial Neural Networks (ANN) and Support Vector Machines (SVM), than the recognition results were compared. We select Error Backpropagation, Resilient Backpropagation and Scaled Conjugate Gradients as ANN training methods, besides selected SVM kernel types are lineer, RBF and polynomial. The experimental results shows us the SVM has beter train and test performance with respect to ANN.