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Multi-Font and Multi-Size Kannada Character Recognition based on the Curvelets and Standard

by Mamatha H. R., Sucharitha S., Srikanta Murthy K.
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 35 - Number 11
Year of Publication: 2011
Authors: Mamatha H. R., Sucharitha S., Srikanta Murthy K.
10.5120/4443-6204

Mamatha H. R., Sucharitha S., Srikanta Murthy K. . Multi-Font and Multi-Size Kannada Character Recognition based on the Curvelets and Standard. International Journal of Computer Applications. 35, 11 ( December 2011), 1-8. DOI=10.5120/4443-6204

@article{ 10.5120/4443-6204,
author = { Mamatha H. R., Sucharitha S., Srikanta Murthy K. },
title = { Multi-Font and Multi-Size Kannada Character Recognition based on the Curvelets and Standard },
journal = { International Journal of Computer Applications },
issue_date = { December 2011 },
volume = { 35 },
number = { 11 },
month = { December },
year = { 2011 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume35/number11/4443-6204/ },
doi = { 10.5120/4443-6204 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:21:41.169419+05:30
%A Mamatha H. R.
%A Sucharitha S.
%A Srikanta Murthy K.
%T Multi-Font and Multi-Size Kannada Character Recognition based on the Curvelets and Standard
%J International Journal of Computer Applications
%@ 0975-8887
%V 35
%N 11
%P 1-8
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Good features are those whose values are similar for objects belonging to the same category and distinct for objects in different categories .In this paper, an attempt is made to develop an algorithm for recognition of machine printed isolated Kannada vowels of different font, size and style using fast discrete curvelet transform. A standard deviation is applied to the coefficients obtained and the result of this is used as the feature vector. In addition, the features are obtained by applying the curvelet transform with different scales. A k-NN classifier is adopted for classification. The proposed algorithm is experimented on 5850 samples of vowels. The recognition is independent of the font and size of the printed characters and the system is seen to deliver reasonable recognition accuracies for different scales with 90.17% being the highest for a combination of scales 1, 2, 3.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Curvelets Standard deviation Kannada character recognition k-NN classifier

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