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

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.

[1]  Hamid Hassanpour,et al.  Using Hidden Markov Models for paper currency recognition , 2009, Expert Syst. Appl..

[2]  Bo Jiang,et al.  Research on paper currency recognition by neural networks , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).

[3]  Jorge Herbert de Lira,et al.  Two-Dimensional Signal and Image Processing , 1989 .

[4]  Davide Rocchesso,et al.  A Fast Mellin and Scale Transform , 2007, EURASIP J. Adv. Signal Process..

[5]  Jae S. Lim,et al.  Two-Dimensional Signal and Image Processing , 1989 .

[6]  William K. Pratt,et al.  Digital image processing, 2nd Edition , 1991, A Wiley-Interscience publication.

[7]  Kazuyuki Murase,et al.  A Paper Currency Recognition System Using Negatively Correlated Neural Network Ensemble , 2010, J. Multim..

[8]  Enrique Frías-Martínez,et al.  Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition , 2006, Eng. Appl. Artif. Intell..

[9]  Fumiaki Takeda,et al.  Multiple kinds of paper currency recognition using neural network and application for Euro currency , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[10]  Anna Vilà,et al.  Development of a fast and non-destructive procedure for characterizing and distinguishing original and fake euro notes , 2006 .

[11]  Leon Cohen,et al.  The scale representation , 1993, IEEE Trans. Signal Process..

[12]  Daijin Kim,et al.  Face recognition using the embedded HMM with second-order block-specific observations , 2003, Pattern Recognit..

[13]  Dustin Boswell,et al.  Introduction to Support Vector Machines , 2002 .

[14]  Hamid Hassanpour,et al.  Feature extraction for paper currency recognition , 2007, 2007 9th International Symposium on Signal Processing and Its Applications.

[15]  J. Todd.  Book Review: Digital image processing (second edition). By R. C. Gonzalez and P. Wintz, Addison-Wesley, 1987. 503 pp. Price: £29.95. (ISBN 0-201-11026-1) , 1988 .