Rotation, scale and translation invariant handwritten Devanagari numeral character recognition using general fuzzy neural network

Authors:
P. M. Patil;T. R. Sontakke
Affiliations:
Department of Electronics, Vishwakarma Institute of Technology, 666, Upper Indira Nagar, Bibvewadi, Pune, Maharashtra 411 037, India;Department of Electronics, Vishwakarma Institute of Technology, 666, Upper Indira Nagar, Bibvewadi, Pune, Maharashtra 411 037, India
Venue:
Pattern Recognition
Year:
2007

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Abstract

In this paper a general fuzzy hyperline segment neural network is proposed [P.M. Patil, Pattern classification and clustering using fuzzy neural networks, Ph.D. Thesis, SRTMU, Nanded, India, January 2003]. It combines supervised and unsupervised learning in a single algorithm so that it can be used for pure classification, pure clustering and hybrid classification/clustering. The method is applied to handwritten Devanagari numeral character recognition and also to the Fisher Iris database. High recognition rates are achieved with less training and recall time per pattern. The algorithm is rotation, scale and translation invariant. The recognition rate with ring data features is found to be 99.5%.