Handwritten digit recognition using combination of neural network classifiers

Author

Khofanzad, A. ; Chung, C.

Author_Institution

Dept. of Electr. Eng., Southern Methodist Univ., Dallas, TX, USA

fYear

1998

fDate

5-7 Apr 1998

Firstpage

168

Lastpage

173

Abstract

A new classification scheme for handwritten digit recognition is proposed. The method is based on combining the decisions of two multilayer perceptron (MLP) artificial neural network classifiers operating on two different feature types. The first feature set is defined on the pseudo Zernike moments of the image whereas the second feature type is derived from the shadow code of the image using a newly defined projection mask. A MLP network is employed to perform the combination task. The performance is tested on a data base of 15000 samples and the advantage of the combination approach is demonstrated

Keywords

character recognition; feature extraction; image classification; multilayer perceptrons; artificial neural network; decision combination; feature set; handwritten digit recognition; image classification scheme; multilayer perceptron; neural network classifiers; projection mask; pseudo Zernike moments; shadow code; Artificial neural networks; Feature extraction; Feedforward systems; Handwriting recognition; Multilayer perceptrons; Neural networks; Pattern classification; Pattern recognition; Shape; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Analysis and Interpretation, 1998 IEEE Southwest Symposium on

Conference_Location

Tucson, AZ

Print_ISBN

0-7803-4876-1

Type

conf

DOI

10.1109/IAI.1998.666880

Filename

666880