Similarity calculator comprising a buffer for a single input pattern

Abstract

A similarity calculator for calculating a set of similarity measures S(A(u, m), B.sup.c)'s according to the technique of dynamic programming comprises an input pattern buffer for successively producing input pattern feature vectors of an input pattern A to be pattern matched with reference patterns B.sup.c, an m-th input pattern feature vector a.sub.m at a time. The similarity measure set is for a set of fragmentary patterns A(u, m)'s defined by a common end point m and start points u's predetermined relative to the end point m. Scalar products (a.sub.m .multidot.b.sub.j.sup.n) are calculated between the m-th input pattern feature vector and reference pattern feature vectors b.sub.j.sup.n of an n-th reference pattern B.sup.n and stored in a scalar product buffer. Recurrence values are calculated according to a recurrence formula for each end point m, rather than for each fragmentary pattern set, and for each reference pattern B.sup.n to provide a similarity measure subset S(A(u, m), B.sup.n)'s, with a recurrence value for each reference pattern feature vector b.sub.v calculated by the use of the scalar product (a.sub.m .multidot.b.sub.v) and recurrence values calculated for a previous end point (m-1) and for at least three consecutive reference pattern feature vectors preselected relative to that reference pattern feature vector b.sub.v. Instead of the scalar product, it is possible to use any one of other measures representative of a similarity or a dissimilarity between an input pattern feature vector and a reference pattern feature vector.