Handwritten Signature Verification using Instance Based Learning

International Journal of Computer Trends and Technology (IJCTT)          
© March to April Issue 2011 by IJCTT Journal
Volume-1 Issue-1                          
Year of Publication : 2011
Authors :Priya Metri , Ashwinder Kaur


Priya Metri , Ashwinder Kaur. "Handwritten Signature Verification using Instance Based Learning"International Journal of Computer Trends and Technology (IJCTT),V1(1):52-55 March to April Issue 2011 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract— For authentication and authorization in legal matter humans are recognized by their Signature. Every human being has their own writing style and hence their signature is used in the financial domain for identity verification. So it is necessary to develop a technique which is efficient in verifying the Handwritten Signature is correct or forge . This paper presents a technique of Handwritten Signature Verification based on Correlation between Handwritten Signature images using feature extracted from it. In this paper we have proposed a method to extract features from scanned image of signatures store it in database. We correlate features of all sample signatures for each person . Then we have to find a mean value from the correlation value of one person signature then compute deviation from it which is used for verification.


[1][Aha, 1989] D. W. Aha. Incremental, instance-based learning of independent and graded concept descriptions. In Sixth International Workshop on Machine Learning, Detroit, MI, 1989. Morgan Kaufmann.
[2] Md. It rat Bin Shams, “Signature Recognition by Segmentation and Regular Line Detection” TENCON 2007 -2007 IEEE Region 10 Conference Volume, Issue, Page(s):1 – 4, Oct. 30, 2007- Nov. 2, 2007.
[3] Neural Network-based Handwritten Signature Verification Alan McCabe, Jarrod Trevathan and Wayne Read School of Mathematics, Physics and Information Technology, James Cook University, Australia Email: This email address is being protected from spambots. You need JavaScript enabled to view it., fjarrod .trevathan, This email address is being protected from spambots. You need JavaScript enabled to view it., JOURNAL OF COMPUTERS, VOL. 3, NO. 8, AUGUST 2008
[4] Alan McCabe, Jarrod Trevathan,“Handwritten Signature Verification Using Complementary Statistical Models”, JOURNAL OF COMPUTERS, VOL. 4, NO. 7, pp: 670- 680,JULY 2009
[5] Debnath Bhattacharyya, and Tai-hoon Kim “Design of Artificial Neural Network for Handwritten Signature Recognition” INTERNATIONAL JOURNAL OF COMPUTERS AND COMMUNICATIONS Issue 3, Volume 4, 2010 International Journal of Computer Trends and Technology- March to April Issue 2011 ISSN:2231-2803 - 4 - IJCTT
[6] Noise-Tolerant Instance-Based Learning Algorithms David W. Aha and Dennis Kibler* Department of Information and Computer Science University of California, Irvine, CA 92717 This email address is being protected from spambots. You need JavaScript enabled to view it. kiblerOics.uci.edu
[7] Samit Biswas1, Tai-hoon Kim2.*, Debnath Bhattacharyya2 “Features Extraction and Verification of Signature Image using Clustering Technique” International Journal of Smart Home Vol.4, No.3, July, 2010
[8] Joarder Kamruzzaman and S. M. Aziz, “A Neural Network BasedCharacter Recognition System Using Double Backpropagation”, Malaysian Journal of Computer Science, Vol. 11 No. 1, June 1998.
[9] Andrew T. Wilson, “Off-line Handwriting Recognition Using Artificial Neural Networks”, University of Minnesota, Morris.
[10] Jens Langner, “Neuronal Network based recognition system of leaf images”, http://www.jens-langner.de/, Last accessed on December 02,Last accessed on December 02, 2010.
[11] Berend-Jan van der Zwaag, “Handwritten Digit Recognition: A Neural Network Demo”, International Conference on Computational Intelligence Theory and Applications, Dortmund, Germany, October 1-3, 2001, LNCS, Vol. 2206/2001, pp. 762-771.
[12] Joao Ricardo Bittencourt, Fernando Santos Osorio, “Adaptive Filters for Image Processing based on Artificial Neural Networks”, XIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI.00), Gramado , Brazil, October 17-20, 2000, on page: 336.
[13] Peter Pivonka, and P. Nepevny, “Generalized Predictive Control with Adaptive Model Based on Neural Networks”, Proceedings of the 6th WSEAS International Conference on NEURAL NETWORKS, Lisbon, Portugal, June 16-18, 2005, pp. 1-4.
[14] S. Sureerattanan, Huynh Ngoc Phien, N. Sureerattanan, and Nikos E. Mastorakis, “The Optimal Multi-layer Structure of Backpropagation Networks”, Proceedings of the 7th WSEAS International Conference on Neural Networks, Cavtat, Croatia, June 12-14, 2006, pp.108-113.
[15] Stergios Papadimitrioy Konstantinos Terzidis, “Classification Process Analysis of Bioinformatics Data with a Support Vector Fuzzy Inference System”, Proceedings of the 8th WSEAS International Conference on Neural Networks, Vancouver, British Columbia, Canada, June 19-21,2007, pp. 90-95.

Keywords— Instance-Based Learning Algorithms, Correlation, Normalization, Signature verification, Feature extraction, Vertical Projection , Horizontal Projection , Diagonal Projection.