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Offline Handwritten Gurmukhi Character Recognition using Particle Swarm Optimized Neural Network

Published on September 2016 by Jaspreet Kaur, B. S. Dhaliwal, S.s Gill
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International Conference on Advances in Emerging Technology
Foundation of Computer Science USA
ICAET2016 - Number 12
September 2016
Authors: Jaspreet Kaur, B. S. Dhaliwal, S.s Gill
2dc22470-22a0-4835-a404-4430c90ea7e4

Jaspreet Kaur, B. S. Dhaliwal, S.s Gill . Offline Handwritten Gurmukhi Character Recognition using Particle Swarm Optimized Neural Network. International Conference on Advances in Emerging Technology. ICAET2016, 12 (September 2016), 35-41.

@article{
author = { Jaspreet Kaur, B. S. Dhaliwal, S.s Gill },
title = { Offline Handwritten Gurmukhi Character Recognition using Particle Swarm Optimized Neural Network },
journal = { International Conference on Advances in Emerging Technology },
issue_date = { September 2016 },
volume = { ICAET2016 },
number = { 12 },
month = { September },
year = { 2016 },
issn = 0975-8887,
pages = { 35-41 },
numpages = 7,
url = { /proceedings/icaet2016/number12/25957-t199/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Advances in Emerging Technology
%A Jaspreet Kaur
%A B. S. Dhaliwal
%A S.s Gill
%T Offline Handwritten Gurmukhi Character Recognition using Particle Swarm Optimized Neural Network
%J International Conference on Advances in Emerging Technology
%@ 0975-8887
%V ICAET2016
%N 12
%P 35-41
%D 2016
%I International Journal of Computer Applications
Abstract

The offline handwritten character recognition is the frontier area of research from last few decades in pattern recognition. It is difficult to recognize handwritten characters as compared to printed characters because of the varying writing styles of individuals. The massive work has been done in languages like Devnagri and Chinese character recognition. The area of Gurmukhi character recognition is even though not new but the problem lies when it comes to look alike and unique characters where the system lacks. In this proposed work, 35 different character samples are used for recognition. The samples have been taken on a plain paper in an isolated manner. After the pre-processing of particular character feature extraction technique is applied. The technique used for feature extraction is Gabor filter. Then ANN is applied for character recognition and if ANN fails to recognize, then character is recognized with the help of PSONN. This improves the overall efficiency of the character recognition system. By training the classifier with whole dataset we obtained 100% accuracy for the given samples.

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Index Terms

Computer Science
Information Sciences

Keywords

Optical Character Recognition Particle Swarm Optimization Handwriting Recognition Gurmukhi Characters Artificial Neural Network Handwritten Character Recognition.