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Handwritten Gurmukhi Numeral Recognition using Different Feature Sets

by Kartar Singh Siddharth, Renu Dhir, Rajneesh Rani
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 28 - Number 2
Year of Publication: 2011
Authors: Kartar Singh Siddharth, Renu Dhir, Rajneesh Rani
10.5120/3361-4640

Kartar Singh Siddharth, Renu Dhir, Rajneesh Rani . Handwritten Gurmukhi Numeral Recognition using Different Feature Sets. International Journal of Computer Applications. 28, 2 ( August 2011), 20-24. DOI=10.5120/3361-4640

@article{ 10.5120/3361-4640,
author = { Kartar Singh Siddharth, Renu Dhir, Rajneesh Rani },
title = { Handwritten Gurmukhi Numeral Recognition using Different Feature Sets },
journal = { International Journal of Computer Applications },
issue_date = { August 2011 },
volume = { 28 },
number = { 2 },
month = { August },
year = { 2011 },
issn = { 0975-8887 },
pages = { 20-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume28/number2/3361-4640/ },
doi = { 10.5120/3361-4640 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:13:42.843999+05:30
%A Kartar Singh Siddharth
%A Renu Dhir
%A Rajneesh Rani
%T Handwritten Gurmukhi Numeral Recognition using Different Feature Sets
%J International Journal of Computer Applications
%@ 0975-8887
%V 28
%N 2
%P 20-24
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recently there is an emerging trend in the research to recognize handwritten characters and numerals of many Indian languages and scripts. In this manuscript we have practiced the recognition of handwritten Gurmukhi numerals. We have used three different feature sets. First feature set is comprised of distance profiles having 128 features. Second feature set is comprised of different types of projection histograms having 190 features. Third feature set is comprised of zonal density and Background Directional Distribution (BDD) forming 144 features. The SVM classifier with RBF (Radial Basis Function) kernel is used for classification. We have obtained the 5-fold cross validation accuracy as 99.2% using second feature set consisting of 190 projection histogram features. On third and first feature sets recognition rates 99.13% and 98% are observed. To obtain better results pre-processing of noise removal and normalization processes before feature extraction are recommended, which are also practiced in our approach.

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Index Terms

Computer Science
Information Sciences

Keywords

Handwritten Gurmukhi numeral recognition Zonal density Projection histogram Distance Profiles Background Directional Distribution (BDD) SVM classifier RBF kernel