Off-Line Handwritten Character Recognition System Using Support Vector Machine
American Journal of Neural Networks and Applications
Volume 3, Issue 2, April 2017, Pages: 22-28
Received: Oct. 24, 2017; Accepted: Nov. 21, 2017; Published: Dec. 13, 2017
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Authors
Gauri Katiyar, Department of Electrical and Electronics Engineering, ITS Engineering College, Gr. Noida (U.P.), India
Ankita Katiyar, School of Computer Science and Engineering, VIT University, Vellore (T.N.), India
Shabana Mehfuz, Department of Electrical Engineering, Jamia Millia Islamia, New Delhi, India
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Abstract
Selection of classifiers and feature extraction methods has a prime role in achieving best possible classification accuracy in character recognition system. Issues of character recognition system related to choice of classifiers and feature extraction methods can be resolved through these objectives. In this proposed work an efficient Support Vector Machine based off-line handwritten character recognition system has been developed. The experiments have been performed using well known standard database acquired from CEDAR, also seven different approaches of feature extraction techniques have been proposed to construct the final feature vector. It is evident from the experimental results that the performance of Support Vector Machine outperforms other state of art techniques reported in literature.
Keywords
Handwritten Character Recognition, Support Vector Machine, Multi Layer Perceptron, And Feature Extraction
To cite this article
Gauri Katiyar, Ankita Katiyar, Shabana Mehfuz, Off-Line Handwritten Character Recognition System Using Support Vector Machine, American Journal of Neural Networks and Applications. Vol. 3, No. 2, 2017, pp. 22-28. doi: 10.11648/j.ajnna.20170302.12
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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