ANN Based Currency Recognition System using Compressed Gray Scale and Application for Sri Lankan Currency Notes - SLCRec

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.

Authors:

• D. A. K. S. Gunaratna
• N. D. Kodikara
• H. L. Premaratne

Keywords:

• Artificial intelligence
• linear transformation and pattern recognition.

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.