A System for Verification of Offline English Signature Using Soft Computing Approach

International Journal of Computer Science and Engineering
© 2015 by SSRG - IJCSE Journal
Volume 2 Issue 9
Year of Publication : 2015
Authors : Rishikant Sagar, Akhilesh Pandey

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How to Cite?

Rishikant Sagar, Akhilesh Pandey, "A System for Verification of Offline English Signature Using Soft Computing Approach," SSRG International Journal of Computer Science and Engineering , vol. 2,  no. 9, pp. 9-13, 2015. Crossref, https://doi.org/10.14445/23488387/IJCSE-V2I9P102

Abstract:

The signature has been crucial tool for authentication of any specified person. In the current era, it has been instrumental in the checking out the forgery and frauds. The whole of the database is secured online through digital signature and other biometrics. However, the stat says that the banks and the other financial institution faces the signature verification problems on the cheques, DDs, and other documents. So, the offline signature verification is indispensable equipments for countering the fakes and other forgery. This means that the signature as a proof of declared test signature template. Euclidian distance in the feature space between the claimed signature and the template serves as a measure of similarity between the two. If this distance is less than a pre-defined threshold (corresponding to minimum acceptable degree of similarity), the test signature is verified to be that of the claimed subject else detected as a forgery. This paper presents a method Offline Signature Verification using a set of simple geometric features based. The features that are used are Baseline Slant Angle, Aspect Ratio, Normalized Area, Center of Gravity and the Slope of the line joining the Centers of Gravity of two halves of a signature image.

Keywords:

Offline, Online,Chque,, DTW, DD.

References:

[1] Ismail, M.Hajjar and M.Quafafou, “Off-line signature verification uses single and parallel Processing”, in Proc. 2009 Intl Conf: Sciences of Electronic, Technologies of Information and Telecommunications, 2009, pp. 1-6.
[2] S.Biswas, T-H Kim and D. Bhattacharyya, “Features extraction and verification of signature image using clustering technique”, International Journal of Smart Home, vol.4, no.3, pp. 43-55, 2010.
[3] Fang, B., Leung, C.H., Tang, Y.Y., Kwok, P.C.K., Tse, K.W., and Wong, Y.K. (2002).Off-Line Signature Verification with Generated Training Samples. IEE Proceedings -Vision, Image and Signal Processing, vol. 149, no. 2, pp. 85 - 90.
[4] Fang, B., Leung, C.H., Tang, Y.Y., Tse, K.W., Kwok, P.C.K., and Wong, Y.K. (2003).Off-line signature verification by tracking of feature and stroke positions. Pattern Recognition, vol. 36, pp. 91-101.
[5] Huang, K. and Yan, H. (2002).Off-Line signature verification using structural feature Correspondence. Pattern Recognition, vol. 35, pp. 2467-2477.
[6] Xiao, X. and Leedham, G (2002). Signature verification using a modified Bayesian network. Pattern Recognition, vol. 35, no. 5, pp. 983-995.
[7] A. Ismail, M.Hajjar and M.Quafafou, “Off-line signature verification uses single and parallel processing”, in Proc. 2009 Intl Conf: Sciences of Electronic, Technologies of In formationand Telecommunications, 2009, pp. 1-6.
[8]S.Biswas, T-H Kim and D. Bhattacharyya, “Features extraction and verification of signature image using clustering technique”, International Journal of Smart Home, vol.4, no.3, pp. 43-55, 2010.
[9] R. Plamondon and S.N.Srihari. 2000. “On-line and offline Handwriting Recognition: A comprehensive Survey”, IEE tran. on Pattern Analysis and Machine Intelligence, Vol. 22, no.1, pp.63-84.
[10] F. Leclerc and R. Plamondon. 1994. “Automatic Verification and Writer Identification: The State of the Art 1989-1993”, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 8, pp. 643 – 660.
[11] R. Sabourin.1997. “Off-line signature verification: Recent advances and perspectives”, BSDIA‟97, pp.84- 98.
[12] J. P. Edward. 2002. “Customer Authentication-The Evolution of Signature Verification in Financial Institutions”, Journal of Economic Crime Management, Volume 1, Issue 1.
[13] J. Coetzer, B.M. Herbst and J.A. Du Preez. 2004. “Offline Signature Verification Using the Discrete Radon Transform and a Hidden Markov Model”, Eurasip Journal on Applied Signal Processing - Special Issue on Biometric Signal Processing, Vol. 100, No. 4, pp. 559- 571.
[14] V. Kiani, R. Pourreza and H. R Pourreza. 2010. “Offline Signature Verification Using Local Radon Transform and Support Vector Machines”, International journal of Image Processing (IJIP) Vol.(3), Issue(5).
[15] V.V Kohir and U.B. Desai. 1998 “ Face Recognition UsingA DCT-HMM Approach”, Fourth IEEE workshop on Applications of Computer vision (WACV‟98).
[16] E. Yacoubi, E.J.R. Justino, R. Sabourin and F. Bortolozzi. 2000. “Off-line signature verification using HMMs and crossvalidation”, IEEE International Workshop in Neural Networks for Signal Processing, pp. 859-868.