Indian Journal of Science and Technology
Year: 2015, Volume: 8, Issue: 10, Pages: 913-918
Seyed Aliakbar Mousavi 1* , Majid Meghdadi 2 , Zahra Hanifeloo2 , Putra Sumari 1 and Muhammad Rafie Mohd Arshad1
1 School of Computer Science, Universiti Sains Malaysia (USM), Malaysia; [email protected]
2 Computer Department, Faculty of Engineering, University of Zanjan, Iran
This paper provides a fast method to recognize a variety of Persian banknotes at different scales. In this technique, the PCA, LDA and sparse representation methods are utilized at feature extraction stage and follows with MLP neural networks, LVQ and SOM in classification. Finally, the application of sparse matrix representation method and combination of both SOM and LVQ neural networks would lead to the best efficiency with precision of 91.15% in recognition of Persian banknotes particularly the worn ones.
Keywords: Banknote Recognition, LVQ, Neural Network, PCA, Sparse Representation
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