As signature is the primary mechanism both for authentication and authorization in legal transactions, the need for efficient auto-mated solutions for signature verification has increased . Unlike a password, PIN, PKI or key cards – identification data that can be forgotten, lost, stolen or shared – the captured values of the handwritten signature are unique to an individual and virtually impossible to duplicate. The primary advantage that signature verification systems have over other type’s technologies is that signatures are already accepted as the common method of identity verification . A signature verification system and the techniques used to solve this problem can be divided into two classes Online and Off-line .On-line approach uses an electronic tablet and a stylus connected to a computer to extract information about a signature and takes dynamic information like pressure, velocity, speed of writing etc. for verification purpose. Whereas Off- line signature verification involves less electronic control and uses signature images captured by scanner or camera. An offline signature verification system uses features extracted from scanned signature image. In this only the pixel image needs to be evaluated.
1. R. Plamondon and S.N. Srihari, "Online and Offline Handwriting Recognition: A Comprehensive Survey", IEEE Tran. on Pattern Analysis and Machine Intelligence, vol.22 no.1, pp.63-84, Jan.2000
2. B. Herbst. J. Coetzer. and J. Preez, “Online Signature Verification Using the Discrete Radon Transform and a Hidden Markov Model,” EURASIP.Journal on Applied Signal Processing, vol. 4, pp. 559–571, 2004.
3. JunLin chen; wen, jing; “Video-Based Signature Verification by Tracking Pen Tip Using Particle Filter Combined with Template Matching” IEEE Conference 2009 , vol. 1 PP. 83 - 88
4. Martinez, L.E., Travieso, C.M, Alonso, J.B., and Ferrer, M. Parameterization of a forgery Handwritten Signature Verification using SVM. IEEE 38thAnnual 2004 International Carnahan Conference on Security Technology ,2004 PP.193-196
5. Vielhauer.c, Mayerhoper.A “Biometric hash based on statistical features of online signatures” IEEE Conference 2002, vol. 1 PP. 123 - 126
6. Prashanth CR,KB Raja,KR Venugopal, LM Patnaik,”Standard Scores Correlation based Offline signature verification system”, International Conference on advances in computing, control and telecommunication Technologies 2009
7. M. Blumenstein. S. Armand. and Muthukkumarasamy, “Off-line Signature Verification using the Enhanced Modified Direction Feature and Neural based Classification,” International Joint Conference on Neural Networks, 2006.
9. Ramachandra A. C ,Jyoti shrinivas Rao”Robust Offline signature verification based on global features” IEEE International Advance Computing Conference ,2009.
10. Ashwini Pansare, Shalini Bhatia “Handwritten Signature Verification using Neural Network” International Journal of Applied Information Systems (IJAIS) – ISSN : 2249-0868 Foundation of Computer Science FCS, New York, USA Volume 1– No.2, January 2012