Recognition of paper currencies by hybrid neural network

For the recognition of paper currencies by image processing, the two steps data processing approach can yield high performance. The two steps include “recognition” and “verification” steps. In the current recognition machine, a simple statistical test is used as the verification step, where univariate Gaussian distribution is employed. Here we propose the use of the probability density formed by a multivariable Gaussian function, where the input data space is transferred to a lower dimensional subspace. Due to the structure of this model, we refer the total processing system as a hybrid neural network. Since the computation of the verification model only needs the inner product and square, the computational load is very small. In this paper, the method and numerical experimental results are shown by using the real data and the recognition machine

Digital Object Identifier: 10.1109/IJCNN.1998.687121
Published with permission from the copyright holder. This is the institute's copy, as published in Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on, 4-9 May 1998, Vol. 3, Pages 1748-1753.
Publisher URL:http://dx.doi.org/10.1109/IJCNN.1998.687121
Copyright © 1998 IEEE. All rights reserved.