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Article:Offline Signature Recognition using Hidden Markov Model (HMM)

by Dr. S. Adebayo Daramola, Prof. T. Samuel Ibiyemi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 10 - Number 2
Year of Publication: 2010
Authors: Dr. S. Adebayo Daramola, Prof. T. Samuel Ibiyemi
10.5120/1454-1967

Dr. S. Adebayo Daramola, Prof. T. Samuel Ibiyemi . Article:Offline Signature Recognition using Hidden Markov Model (HMM). International Journal of Computer Applications. 10, 2 ( November 2010), 17-22. DOI=10.5120/1454-1967

@article{ 10.5120/1454-1967,
author = { Dr. S. Adebayo Daramola, Prof. T. Samuel Ibiyemi },
title = { Article:Offline Signature Recognition using Hidden Markov Model (HMM) },
journal = { International Journal of Computer Applications },
issue_date = { November 2010 },
volume = { 10 },
number = { 2 },
month = { November },
year = { 2010 },
issn = { 0975-8887 },
pages = { 17-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume10/number2/1454-1967/ },
doi = { 10.5120/1454-1967 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:58:44.427690+05:30
%A Dr. S. Adebayo Daramola
%A Prof. T. Samuel Ibiyemi
%T Article:Offline Signature Recognition using Hidden Markov Model (HMM)
%J International Journal of Computer Applications
%@ 0975-8887
%V 10
%N 2
%P 17-22
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

HMM has been used successfully to model speech and online signature in the past two decades. The success has been attributed to the fact that these biometric traits have time reference. Only few HMM based offline signature recognition systems have be developed because offline signature lack time reference. This paper presents a recognition system for offline signatures using Discrete Cosine Transform (DCT) and Hidden Markov Model (HMM). The signature to be trained or recognized is vertically divided into segments at the centre of gravity using the space reference positions of the pixels. The number of segmented signature blocks is equal to the number of states in the HMM for each user notwithstanding the length of the signatures. Experimental result shows that successful signatures recognition rates of 99.2% is possible. The result is better in comparison with previous related systems based on HMM and statistical classifiers.

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Index Terms

Computer Science
Information Sciences

Keywords

Offline Signature DCT Features Hidden Markov Model

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