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Kannada Handwritten numeral Recognition using FFBPNN Classifiers

by Ashoka H N, Manjaiah D H, Rabindranath Bera
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 91 - Number 5
Year of Publication: 2014
Authors: Ashoka H N, Manjaiah D H, Rabindranath Bera
10.5120/15877-4838

Ashoka H N, Manjaiah D H, Rabindranath Bera . Kannada Handwritten numeral Recognition using FFBPNN Classifiers. International Journal of Computer Applications. 91, 5 ( April 2014), 17-21. DOI=10.5120/15877-4838

@article{ 10.5120/15877-4838,
author = { Ashoka H N, Manjaiah D H, Rabindranath Bera },
title = { Kannada Handwritten numeral Recognition using FFBPNN Classifiers },
journal = { International Journal of Computer Applications },
issue_date = { April 2014 },
volume = { 91 },
number = { 5 },
month = { April },
year = { 2014 },
issn = { 0975-8887 },
pages = { 17-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume91/number5/15877-4838/ },
doi = { 10.5120/15877-4838 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:11:58.763961+05:30
%A Ashoka H N
%A Manjaiah D H
%A Rabindranath Bera
%T Kannada Handwritten numeral Recognition using FFBPNN Classifiers
%J International Journal of Computer Applications
%@ 0975-8887
%V 91
%N 5
%P 17-21
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents the performance of Kannada handwritten numeral recognition using feed forward back propagation neural network (FFBPNN) classifiers. The classifier is designed to recognize the Kannada handwritten numerals. Samples are represented by the few features extracted by the zoning technique. The input numeral samples in binary form are stored in a fixed window size of 12x12 and partitioned into nine sub regions of 4x4 sizes for their representation. A normalized feature value is computed by the one's present each sub region for their representation in two different approaches. On experimentation, it is found the overall recognition rate of 99. 7% and 95. 5% for the feature extraction approaches M1 and M2 respectively.

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Index Terms

Computer Science
Information Sciences

Keywords

Feature extraction Knowledge base Kannada handwritten numeral recognition

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