HIGH-SPEED PAPER CURRENCY RECOGNITION BY NEURAL NETWORKS

In this paper a new technique is proposed to improve the recognition a bility and the transaction speed to classify the Japanese and U.S. pap er currency. Two types of data sets, time series data and Fourier powe r spectra, are used in this study. In both cases, they are directly us ed as inputs to the neural network. Still more we also refer a new eva luation method of recognition ability. Meanwhile, a technique is propo sed to reduce the input scale of the neural network without preventing the growth of recognition. This technique uses only a subset of the o riginal data set which is obtained using random masks. The recognition ability of using large data set and a reduced data set are discussed. In addition to that the results of using a reduced data set of the Fo urier power spectra and the time series data are compared.