Handwriting character classification using Freeman’s olfactory KIII model

Abstract

KIII model is an olfactory model proposed by W. J. Freeman referring to a physiological structure of mammal’s olfactory system. The KIII model has been applied to kinds of pattern recognition systems, for example, electronic nose, tea classification, etc. However, the dynamics of neurons in the KIII model is given by Hodgkin-Huxley’s second-order differential equation and it consumes a very high computation cost. In this paper, we propose a simplified dynamics of chaotic neuron instead of the Hodgkin-Huxley dynamics at first, and secondly, we propose to use Fourier transformation with high resolution capability to extract features of time series behaviors of internal states of M1 nodes in KIII model instead of the conventional standard deviation method. Furthermore, paying attention to the point that human brain does visual processing as same as olfactory processing in the sense of information processing, a handwriting image recognition problem is treated as a new application field of KIII model. Through the computer simulation of the handwriting character classification, it is shown that the proposed method is useful by the comparison of experiment results with both computation time and recognition accuracy.