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Abstract

A covariance matrix of a set of m number of column components of a two-dimensional input pattern and a covariance matrix of a set of n number of row components are obtained. The column covariance matrix and a column covariance matrix of a set of column components of a standard pattern belong to the corresponding one of various categories are multiplied and summed to obtain a column multiply-sum data and the row covariance matrix and a row covariance matrix of a set of row components of the standard pattern are multiplied and summed to obtain row multiply-sum data. The column and row multiply-sum data are multiplied by a multiplier to obtain product data. A maximum detector detects a maximum product data from the product data obtained by the multiplier with respect to all categories. The category corresponding to the maximum product data is recognized as belonging to the input pattern.

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