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Gurmukhi Printed Character Recognition using Hierarchical Centroid Method and SVM

by Sandeep Kaur, Rekha Bhatia
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 149 - Number 3
Year of Publication: 2016
Authors: Sandeep Kaur, Rekha Bhatia
10.5120/ijca2016911367

Sandeep Kaur, Rekha Bhatia . Gurmukhi Printed Character Recognition using Hierarchical Centroid Method and SVM. International Journal of Computer Applications. 149, 3 ( Sep 2016), 24-27. DOI=10.5120/ijca2016911367

@article{ 10.5120/ijca2016911367,
author = { Sandeep Kaur, Rekha Bhatia },
title = { Gurmukhi Printed Character Recognition using Hierarchical Centroid Method and SVM },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2016 },
volume = { 149 },
number = { 3 },
month = { Sep },
year = { 2016 },
issn = { 0975-8887 },
pages = { 24-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume149/number3/25977-2016911367/ },
doi = { 10.5120/ijca2016911367 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:54:38.956647+05:30
%A Sandeep Kaur
%A Rekha Bhatia
%T Gurmukhi Printed Character Recognition using Hierarchical Centroid Method and SVM
%J International Journal of Computer Applications
%@ 0975-8887
%V 149
%N 3
%P 24-27
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper the system for the recognition of printed Gurmukhi character is proposed. Hierarchical centroid method is used for extracting the feature from images of printed characters. The main advantage of using this method is that it gives size invariant feature vector and therefore can play important role for manuscript recognition. The dataset used in this study consists of 29 different font styles of the printed characters. The classification is done by using Support Vector Machine. The performance of the classifier is determined by measuring accuracy using 10-fold cross validation procedure. The highest accuracy obtained on SVM is 97.87% with the combination of nu-SVC type and RBF kernel.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Character Recognition Support Vector Machine Printed Gurmukhi.

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