CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

A Novel Paper Currency Recognition using Fourier Mellin Transform, Hidden Markov Model and Support Vector Machine

by Abbas Yaseri, Seyed Mahmoud Anisheh
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 61 - Number 7
Year of Publication: 2013
Authors: Abbas Yaseri, Seyed Mahmoud Anisheh
10.5120/9939-3997

Abbas Yaseri, Seyed Mahmoud Anisheh . A Novel Paper Currency Recognition using Fourier Mellin Transform, Hidden Markov Model and Support Vector Machine. International Journal of Computer Applications. 61, 7 ( January 2013), 17-22. DOI=10.5120/9939-3997

@article{ 10.5120/9939-3997,
author = { Abbas Yaseri, Seyed Mahmoud Anisheh },
title = { A Novel Paper Currency Recognition using Fourier Mellin Transform, Hidden Markov Model and Support Vector Machine },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 61 },
number = { 7 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 17-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume61/number7/9939-3997/ },
doi = { 10.5120/9939-3997 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:08:27.557760+05:30
%A Abbas Yaseri
%A Seyed Mahmoud Anisheh
%T A Novel Paper Currency Recognition using Fourier Mellin Transform, Hidden Markov Model and Support Vector Machine
%J International Journal of Computer Applications
%@ 0975-8887
%V 61
%N 7
%P 17-22
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A paper currency recognition system has a wide range of applications such as self receiver machines for automated teller machines and automatic good-selling machines. In this paper a new paper currency recognition system based on Fourier-Mellin transform, Markovian characteristics and Support Vector Machine (SVM) is presented. In the first, a pre-processing algorithm by Fourier-Mellin transform is performed. The key feature of Fourier-Mellin transform is that it is invariant in rotation, translation and scale of the input image. Then, obtained image is segmented and markovian characteristics of each segment have been utilized to construct a feature vectors. These vectors are then fed into SVM classifier for paper currency recognition. In order to evaluate the effectiveness of the system several experiments are carried out. Experimental result indicates that the proposed method achieved high accuracy rate in paper currency recognition.

References
  1. Debnath, K. K. , Ahmed, S. U. and Shahjahan, M. 2010. A Paper Currency Recognition System Using Negatively Correlated Neural Network Ensemble. JOURNAL OF MULTIMEDIA, VOL. 5, NO. 6, 560-567.
  2. Hassanpour, H. , Yaseri, A. and Ardeshir, G. 2002. Feature Extraction for Paper currency Recognition. In International symposium on signal processing and its applications (ISSPA), Sharjah, UAE, 1-4.
  3. Takeda, F. and Nishikage, T. 2000. Multiple kinds of paper currency recognition using neural network and application for Euro currency. In IEEE International Joint Conference on Neural Networks, Vol. 2, 143–147.
  4. Vila, A. , Ferrer, N. , Mantecon, J. , Breton, D. , and Garcia, J. F. 2006. Development of a fast and non-destructive procedure original and fake euro notes. Analytica Chimica Acta, 559, 257–263.
  5. Zhang, E. H. , Jiang, B. , Duan, J. H. and Bian, Z. Z. 2003. Research on paper currency recognition by neural networks, In Proceeding of the second international conference machine learning and cybernetics.
  6. Hassanpour, H. , Farahabadi, P. M. 2009. Using Hidden Markov Models for paper currency recognition, Expert Systems With Applications , no 36, 10105-10111.
  7. Jae, Lim S. 1990. Two-dimensional signal and image processing. Englewood Cliffs, NJ: Prentice Hall.
  8. Gonzalez, R. C. , and Woods, R. E. 2002. Digital image processing (2nd edition). Prentice Hall.
  9. Sena, A. D. , Rocchesso, D. 2007. A fast Mellin and scale transform, EURASIP journal on Applied Signal Processing, Vol. 2007, 1-9.
  10. Cohen, L. 1993. The scale Representation, IEEE Trans on Signal Processing, Vol. 41, 3275-3291.
  11. Iosifescu, M. 1980. Finite Markov process and their applications. New York, NY: Wiley.
  12. Kim, M. , Kim, D. , and Lee, S. 2003. Face recognition using the embedded HMM with second-order block-specific observations. Pattern Recognition, 36(11), 2723–2733.
  13. Cristianini, N. and Shawe-Taylor, J. 2000. An Introduction to Support Vector Machines. Cambridge University Press, Cambridge.
  14. Frias-Martinez, E. , Sanchez, A. and Velez, J. 2006. Support vector machines versus multi layer perceptrons for efficient off-line signature recognition, Engineering Applications of Artificial Intelligence 19, 693–704.
  15. http://www. banknotes. com.
Index Terms

Computer Science
Information Sciences

Keywords

Paper currency recognition Fourier-Mellin Transform Markovian characteristics Support Vector Machine

Powered by PhDFocusTM