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Reseach Article

Classification of Offline Handwritten Signatures using Wavelets and a Pattern Recognition Neural Network

Published on February 2014 by Poornima G Patil, Ravindra S Hegadi
National Conference on Recent Advances in Information Technology
Foundation of Computer Science USA
NCRAIT - Number 4
February 2014
Authors: Poornima G Patil, Ravindra S Hegadi
ff587083-72cc-4052-bea3-0968e4eeae40

Poornima G Patil, Ravindra S Hegadi . Classification of Offline Handwritten Signatures using Wavelets and a Pattern Recognition Neural Network. National Conference on Recent Advances in Information Technology. NCRAIT, 4 (February 2014), 5-9.

@article{
author = { Poornima G Patil, Ravindra S Hegadi },
title = { Classification of Offline Handwritten Signatures using Wavelets and a Pattern Recognition Neural Network },
journal = { National Conference on Recent Advances in Information Technology },
issue_date = { February 2014 },
volume = { NCRAIT },
number = { 4 },
month = { February },
year = { 2014 },
issn = 0975-8887,
pages = { 5-9 },
numpages = 5,
url = { /proceedings/ncrait/number4/15161-1429/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Recent Advances in Information Technology
%A Poornima G Patil
%A Ravindra S Hegadi
%T Classification of Offline Handwritten Signatures using Wavelets and a Pattern Recognition Neural Network
%J National Conference on Recent Advances in Information Technology
%@ 0975-8887
%V NCRAIT
%N 4
%P 5-9
%D 2014
%I International Journal of Computer Applications
Abstract

The various studies conducted for classification of handwritten signatures of people have shown that the task is difficult because there is intra personal differences among the signatures of the same person. The signatures of the same person vary with time, age of the person and also because of the emotional state of a person. The task of classifying the skilled forgery signatures is all the more challenging because they are the result of lot of practice, closely imitating the signature. Neural networks based classifiers have proved to yield very accurate results. This paper for offline signature verification uses the images stored in the GPDS database. The preprocessed images are decomposed using discrete wavelet transform up to the maximum level. The wavelet energy features corresponding to the approximation and detail along with the approximation and detail coefficients make the feature set. A pattern recognition neural network is designed which classifies the inputs based on the target classes.

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Index Terms

Computer Science
Information Sciences

Keywords

Wavelets Principal Component Analysis Pattern Recognition Neural Network