Learning Vector Quantisation based recognition of offline handwritten signatures
by Gulzar Ali Khuwaja; Mohammad Shakeel Laghari
International Journal of Biometrics (IJBM), Vol. 4, No. 2, 2012

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.

Online publication date: Sat, 29-Nov-2014

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Biometrics (IJBM):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com