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Offline Signature Recognition using Hidden Markov Model (HMM)

Daramola, S. A. and Ibiyemi, T. S (2010) Offline Signature Recognition using Hidden Markov Model (HMM). International Journal of Computer Applications, 10 (2). pp. 17-22. ISSN 0975 – 8887

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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.

Item Type: Article
Uncontrolled Keywords: Offline Signature, DCT Features, Hidden Markov Model.
Subjects: T Technology > T Technology (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User: Mrs Hannah Akinwumi
Date Deposited: 03 Mar 2016 14:51
Last Modified: 03 Mar 2016 14:51

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