COMPARISON OF SVM AND FUZZY CLASSIFIER FOR AN INDIAN SCRIPT

Abstract
With the advent of technological era, conversion of scanned document (handwritten or printed) into machine editable format has attracted many researchers. This paper deals with the problem of recognition of Gujarati handwritten numerals. Gujarati numeral recognition requires performing some specific steps as a part of preprocessing. For preprocessing digitization, segmentation, normalization and thinning are done with considering that the image have almost no noise. Further affine invariant moments based model is used for feature extraction and finally Support Vector Machine (SVM) and Fuzzy classifiers are used for numeral classification. . The comparison of SVM and Fuzzy classifier is made and it can be seen that SVM procured better results as compared to Fuzzy Classifier.

Authors
M. J. Baheti1 and K. V. Kale2
1Department of Computer Science and Engineering, Shri Neminath Jain Brahmacharyashram’s Late Sau. Kantabai Bhavarlalji Jain College of Engineering, Maharashtra, India,2Department of Computer Science and Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Maharashtra, India

Keywords
Support Vector Machine, Fuzzy Classifier, Gujarati Handwritten Numerals
Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 2 , Issue: 2 )
Date of Publication :
January 2012

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