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Recognition of Handwritten Gurmukhi Numeral using Gabor Filters

by Sukhpreet Singh, Renu Dhir
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 47 - Number 1
Year of Publication: 2012
Authors: Sukhpreet Singh, Renu Dhir
10.5120/7150-9852

Sukhpreet Singh, Renu Dhir . Recognition of Handwritten Gurmukhi Numeral using Gabor Filters. International Journal of Computer Applications. 47, 1 ( June 2012), 7-11. DOI=10.5120/7150-9852

@article{ 10.5120/7150-9852,
author = { Sukhpreet Singh, Renu Dhir },
title = { Recognition of Handwritten Gurmukhi Numeral using Gabor Filters },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 47 },
number = { 1 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 7-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume47/number1/7150-9852/ },
doi = { 10.5120/7150-9852 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:40:45.792247+05:30
%A Sukhpreet Singh
%A Renu Dhir
%T Recognition of Handwritten Gurmukhi Numeral using Gabor Filters
%J International Journal of Computer Applications
%@ 0975-8887
%V 47
%N 1
%P 7-11
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Isolated handwritten character recognition has been the subject of intensive research during the last decades because it is useful in wide range of real world problems. It also provides a solution for processing large volumes of data automatically. There is an emerging trend in the research of recognizing handwritten characters and numerals of many Indian languages and scripts. In this paper, two different feature sets based on Gabor filter have been used for recognition. One is being GABM having dimensionality 210 and other being GABN with dimensionality 200. The SVM classifier with RBF (Radial Basis Function) kernel is used for classification. The performance of the Gabor filter is tested on the database consisting of 1500 samples for basic 10 numerals of Gurmukhi script, collected from different writers. By using 7-fold cross validation, accuracy of 99. 53% using second feature set and 98. 4% using first feature set are observed. To obtain better results preprocessing of noise removal and normalization processes before feature extraction are recommended.

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Index Terms

Computer Science
Information Sciences

Keywords

Gabor Features Gabor Filters Handwritten Gurmukhi Numeral Recognition Svm With Rbf Kernel

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