Title: Learning Vector Quantisation based recognition of offline handwritten signatures

Authors: Gulzar Ali Khuwaja; Mohammad Shakeel Laghari

Addresses: Department of Computer Engineering, College of Computer Sciences and Information Technology, King Faisal University P.O. Box 400, Al Ahsa 31982, Saudi Arabia ' Department of Electrical Engineering, College of Engineering, UAE University, P.O. Box 17555, Al Ain, UAE

Abstract: Biometrics, which refers to the identification of an individual based on his or her physiological or behavioural characteristics, has the capability to reliably distinguish between an authorised person and an imposter. This paper presents a low-cost and high-speed Artificial Neural Network (ANN) based offline recognition system of handwritten signatures that is trained with low-resolution and small-sized scanned signature images. The proposed architecture recognises mixed (both Arabic and English) handwritten signatures based on varying parameters and eliminating redundant hidden layer units that learns the correlation of patterns. Empirical results yield an accuracy rate of 98.7% for an unseen 150 signatures of varying covered areas of 10 persons on the network that is trained with another 120 images. The robustness of the proposed algorithm is demonstrated by calculating standard deviation of 50 classifiers.

Keywords: pattern recognition; computer vision; adaptive classification; handwritten signature recognition; artificial neural networks; ANNs; biometrics; scanned signatures; Arabic; English; learning vector quantisation; LVQ.

DOI: 10.1504/IJBM.2012.046246

International Journal of Biometrics, 2012 Vol.4 No.2, pp.116 - 129

Received: 12 Mar 2011
Accepted: 03 Aug 2011

Published online: 25 Mar 2012 *

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