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Proceedings Paper

Study on recognition algorithm for paper currency numbers based on neural network
Author(s): Xiuyan Li; Tiegen Liu; Yuanyao Li; Zhongchuan Zhang; Shichao Deng
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Paper Abstract

Based on the unique characteristic, the paper currency numbers can be put into record and the automatic identification equipment for paper currency numbers is supplied to currency circulation market in order to provide convenience for financial sectors to trace the fiduciary circulation socially and provide effective supervision on paper currency. Simultaneously it is favorable for identifying forged notes, blacklisting the forged notes numbers and solving the major social problems, such as armor cash carrier robbery, money laundering. For the purpose of recognizing the paper currency numbers, a recognition algorithm based on neural network is presented in the paper. Number lines in original paper currency images can be draw out through image processing, such as image de-noising, skew correction, segmentation, and image normalization. According to the different characteristics between digits and letters in serial number, two kinds of classifiers are designed. With the characteristics of associative memory, optimization-compute and rapid convergence, the Discrete Hopfield Neural Network (DHNN) is utilized to recognize the letters; with the characteristics of simple structure, quick learning and global optimum, the Radial-Basis Function Neural Network (RBFNN) is adopted to identify the digits. Then the final recognition results are obtained by combining the two kinds of recognition results in regular sequence. Through the simulation tests, it is confirmed by simulation results that the recognition algorithm of combination of two kinds of recognition methods has such advantages as high recognition rate and faster recognition simultaneously, which is worthy of broad application prospect.

Paper Details

Date Published: 27 January 2009
PDF: 11 pages
Proc. SPIE 7156, 2008 International Conference on Optical Instruments and Technology: Optical Systems and Optoelectronic Instruments, 71560X (27 January 2009); doi: 10.1117/12.807073
Show Author Affiliations
Xiuyan Li, Tianjin Univ. (China)
Tiegen Liu, Tianjin Univ. (China)
Yuanyao Li, Tianjin Univ. (China)
Zhongchuan Zhang, Tianjin Univ. (China)
Shichao Deng, Tianjin Univ. (China)


Published in SPIE Proceedings Vol. 7156:
2008 International Conference on Optical Instruments and Technology: Optical Systems and Optoelectronic Instruments

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