In order to maintain the normal running of economy in China, anti-counterfeiting detection of paper currency has
been an important technology in the coinage company and the bank, but the detection using spectrum for Chinese
paper currency anti-counterfeiting has not been applied in China. A real-time detection method, with broad
spectrum including ultraviolet and infrared wavelengths, is proposed in this paper, which achieves the purpose of
anti-counterfeiting by using anti-fake properties of paper currency's coating surface, through different lights
stimulation the full spectrum light irradiation on currency surface, with its reflection spectrum detected by
spectrometer. The proposed method has such advantages as high technology, high detection precision and easy to
identify, and has been applied to a practical system, which satisfies the real-time requirement.
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.