Offline Signature Verification for Detecting Signature Forgery: A Comparative Study
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2015 by IJCTT Journal|
|Year of Publication : 2015|
|Authors : Anisha Soni, Dharmendra Kumar Roy|
|DOI : 10.14445/22312803/IJCTT-V21P123|
Anisha Soni, Dharmendra Kumar Roy "Offline Signature Verification for Detecting Signature Forgery: A Comparative Study". International Journal of Computer Trends and Technology (IJCTT) V21(3):123-125, March 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
As signature is generally used as a means of individual verification, there is a need for an automatic verification system. Signatures provide a safe means of verification and authorization in authorized documents. However one of the key challenges is the ability of the system to detect skilled and unskilled forgery. Many cases of bank cheque forgeries have been reported. Most of the offline signature verification system adopts recognition based technique where the system classifies a given signature sample as one of the samples from the database. However detection of a forgery in a given sample is challenging as the input sample looks alike to one of the samples in the database. A simple and a consistent system has to be designed which should identify various types of forgeries. Various approaches have been used to implement biometric signature verification some of which are dynamic time warping (DTW), Bayesian Learning, Template Matching Technique, Hidden Markov Model (HMM), Support Vector Machine (SVM) etc. This paper presents a comparative and qualitative study of these methods used for offline signature verification.
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Skilled and Unskilled Forgery, Signature Verification, Forgery detection, Dynamic Time Warping, Bayesian Learning, Template Matching Technique, Hidden Markov Model (HMM), Support Vector Machine (SVM).