IDENTIFICATION OF SIGNATURE USING ZONING METHODS AND SUPPORT VECTOR MACHINE
DOI:
https://doi.org/10.53555/eijse.v4i4.150Keywords:
signature identification, zoning method, SVMAbstract
Identification of signatures is the process of identifying and defining a person’s signature. Identification of signatures including biometics that use natural human behavior. Identification of signatures can be used in security areas such as money withdrawal permits, check validation, credit card transactions and more. During this signature identification is done manually. Difficulty in this way, if the signature to be identified is large, the examiner will experience fatigue. To simplify it needs to be developed to create a computerized signature identification system. In this research, the development of this signature identification is done using the method of zoning and Support Vector Machine (SVM) classification. Based on the tests that have been done, normal test data test resulted in recognition accuracy of 95.31%. In testing the test data with disturbance obtained accuracy of 20.31%. While the testing of artificial signatures generated an accuracy of 70%. In addition to the registered signature image pattern, there are also signature images that are not registered in the database. The accuracy obtained in this test is 100%.
References
. R. Abbas, “A Prototype System for Off-Line Signature Verification Using Multi layered Feedforward Neural,” 1994.
. O. D. Trier, A. K. Jain, and T. Taxt, “Feature Extraction Methods for Character Recognition,” Univ. Oslo, vol. 29, no. 4, pp. 641–662, 1996.
. N. Amelia, “Introduction to Hijaiyah Pattern Using Support Vector Machine(SVM),” 2016.
. S. V Rajashekararadhya, “Efficient Zone Based Feature Extration Algorithm for Handwritten Numeral Recognition of Four Popular South Indian Scripts,” J. Theor. Appl. Inf. Technol., vol. 4, no. 12, pp. 1171–1181, 2008.
. N. Putra, “Value Enhancement of Propagation Network Features Back in the Introduction of Handwriting Numbers Using Zoning and Diagonal Based Feature Extraction,” Sumatera Utara University, 2012.
. I. M. G. Sunarya, “Menggunakan Metode Jaringan Syaraf Tiruan Model Perceptron,” Univ. Pendidik. dan Ganesha, vol. 7, no. 1, pp. 700–710, 2013.
. A. Jariah, “Introduction to Patterns of Signatures UsingMoment Invariant and Radial Basis Function (RBF)Neural Network,” Yogyakarta University, pp. 85–92, 2011.
. D. K. W. Buana, “Signature Identification using Back propagation Neural Network,” pp. 1–8.
. Zaitun, Warsito, “System for Identifying and Recognizing Signs of the Image Pattern Using Artificial Neural Network Systems with Back propagation Method,” vol. 3, no. 2, pp. 93–101, 2015.
. Z. B. Fiqhi, R. R. Isnanto, and M. Somantri, “Identification of Signatures Using Principal Component Analysis and Artificial Neural Network -,” pp. 1–8, 2013.
. A. Wibowo, Wirawan, Y. K. Suprapto, “Signature Verification and Identification Offline using Wavelet dan Learning Vector Quantization,” Inst. Teknol. Sepuluh Nop., vol. 5, no. 1, pp. 649–655, 2014.
. T. Fotak, P. Koruga, and M. Ba, “Online Handwritten Signature Identication : The Basics,” 2011.
Downloads
Published
Issue
Section
License
Copyright (c) 2018 EPH - International Journal of Science And Engineering (ISSN: 2454 - 2016)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
