Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30172
ANN Based Currency Recognition System using Compressed Gray Scale and Application for Sri Lankan Currency Notes - SLCRec

Authors: D. A. K. S. Gunaratna, N. D. Kodikara, H. L. Premaratne

Abstract:

Automatic currency note recognition invariably depends on the currency note characteristics of a particular country and the extraction of features directly affects the recognition ability. Sri Lanka has not been involved in any kind of research or implementation of this kind. The proposed system “SLCRec" comes up with a solution focusing on minimizing false rejection of notes. Sri Lankan currency notes undergo severe changes in image quality in usage. Hence a special linear transformation function is adapted to wipe out noise patterns from backgrounds without affecting the notes- characteristic images and re-appear images of interest. The transformation maps the original gray scale range into a smaller range of 0 to 125. Applying Edge detection after the transformation provided better robustness for noise and fair representation of edges for new and old damaged notes. A three layer back propagation neural network is presented with the number of edges detected in row order of the notes and classification is accepted in four classes of interest which are 100, 500, 1000 and 2000 rupee notes. The experiments showed good classification results and proved that the proposed methodology has the capability of separating classes properly in varying image conditions.

Keywords: Artificial intelligence, linear transformation and pattern recognition.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1081211

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2340

References:


[1] E. Zhang, B. Jiang, J. Duan and Z. Bian, "Research on paper currency recognition by neural networks", in Proc. 2nd International Conf. Machine Learning and Cybernetics, Xi-an, 2003, pp 2193-2196.
[2] F. Takeda and S. Omatu, "A neuro-paper currency recognition method using optimized masks by genetic algorithm". In Proc. IEEE International Conference on Systems, Man and Cybernetics, 1995, pp 4367-4371.
[3] F. Takeda and S. Omatu. Image Processing and Pattern Recognition, Academic Press, 1998, pp 133-160.
[4] F. Takeda and T. Nishikage, "Multiple kinds of paper currency recognition using neural network and application for euro currency". In Proc. IEEE International Joint Conference on Neural Networks, 2000, pp 143-147.
[5] A. Ahmadi, S. Omatu, and T. Kosaka, "A study on evaluating and improving the reliability of bank note neuro-classifiers". In Proc. SICE Annual Conference, Japan, 2003, pp 2550-2554.
[6] A. Ahmadi, S. Omatu, and T. Kosaka. "Improvement of the reliability of bank note classifier machines", 2004, pp 1313-1316.
[7] E. Choia, J. Lee, and J. Yooni. "Feature extraction for banknote classification using wavelet transform", In Proc. 18th International Conference on Pattern Recognition, 2006, pp 934-937.
[8] S. Omatu, T.Fujinaka, T. Kosaka, H. Yanagimoto, and M. Yoshioka. "Italian lira classification by lvq". In Proc. International Joint Conference on Neural Networks, IJCNN, 2001, pp 2947-2951.
[9] T. Kohonen. Self Organizing maps. Springer, Berlin, 1995.
[10] M. Gori, A. Frosini and P. Priami." A neural network-based model for paper currency recognition and verification", pages 1482-1490, 1996.
[11] B Yegnanarayan. Artificial Neural Networks. New Delhi110 001, Prentice-Hall of India, 2005.
[12] J. Smokelin. "Wavelet feature extraction for image pattern recognition". In Proc. SPIE, volume 2751, 1996, pp 110-121.