Study on recognition algorithm for paper currency numbers based on neural network

Xiuyan Li; Tiegen Liu; Yuanyao Li; Zhongchuan Zhang; Shichao Deng

doi:10.1117/12.807073

26 January 2009 Study on recognition algorithm for paper currency numbers based on neural network

Xiuyan Li, Tiegen Liu, Yuanyao Li, Zhongchuan Zhang, Shichao Deng

Author Affiliations +

Proceedings Volume 7156, 2008 International Conference on Optical Instruments and Technology: Optical Systems and Optoelectronic Instruments; 71560X (2009) https://doi.org/10.1117/12.807073
Event: International Conference of Optical Instrument and Technology, 2008, Beijing, China

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.

Citation Download Citation

Xiuyan Li, Tiegen Liu, Yuanyao Li, Zhongchuan Zhang, and Shichao Deng "Study on recognition algorithm for paper currency numbers based on neural network", Proc. SPIE 7156, 2008 International Conference on Optical Instruments and Technology: Optical Systems and Optoelectronic Instruments, 71560X (26 January 2009); https://doi.org/10.1117/12.807073

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE