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Telugu Handwritten Isolated Characters Recognition using Two Dimensional Fast Fourier Transform and Support Vector Machine

by Raju Dara, Urmila Panduga
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 116 - Number 5
Year of Publication: 2015
Authors: Raju Dara, Urmila Panduga
10.5120/20330-0820

Raju Dara, Urmila Panduga . Telugu Handwritten Isolated Characters Recognition using Two Dimensional Fast Fourier Transform and Support Vector Machine. International Journal of Computer Applications. 116, 5 ( April 2015), 7-11. DOI=10.5120/20330-0820

@article{ 10.5120/20330-0820,
author = { Raju Dara, Urmila Panduga },
title = { Telugu Handwritten Isolated Characters Recognition using Two Dimensional Fast Fourier Transform and Support Vector Machine },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 116 },
number = { 5 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 7-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume116/number5/20330-0820/ },
doi = { 10.5120/20330-0820 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:56:12.756584+05:30
%A Raju Dara
%A Urmila Panduga
%T Telugu Handwritten Isolated Characters Recognition using Two Dimensional Fast Fourier Transform and Support Vector Machine
%J International Journal of Computer Applications
%@ 0975-8887
%V 116
%N 5
%P 7-11
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Research in character recognition is an old application in the area of pattern recognition and has attracted many researchers during the last few decades. Handwritten character recognition (HCR) is of two types namely, Online and Offline. The recognition accuracy for HCR is less than 60% as per the literature survey. Also the non existence of standard database for Indian languages is another reason for motivation of this work. This work describes Offline HCR by extracting features using 2D FFT and using the support vector machines for Telugu documents. The best percentage recognition accuracy for Telugu handwritten characters is 71%.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Handwritten character recognition 2D FFT support vector machine Classifier Pattern Recognition.

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