Abstract: This invention relates to the Pattern Recognition (PR) of noisy/inexact strings and sequences and particularly to syntactic Pattern Recognition. The present invention presents a process by which a user can recognize an unknown sting X, which is an element of a finite, but possibly larger Dictionary, H, by processing the information contained in its noisy/inexact version, Y, where Y is assumed to contain substitution, insertion or deletion errors. The recognized string, which is the best estimate X+ of X, is defined as that element of H which minimizes the Generalized Levenshtein Distance D(X, Y) between X and Y, for all X<H. Rather than evaluate D(X5Y) for every X<H sequentially, the present invention achieves this simultaneously for every X<H by representing the Dictionary as a Trie, and searching the Trie using a new Al-based search strategy.

Abstract: This invention relates to the Pattern Recognition (PR) of noisy/inexact strings and sequences and particularly to syntactic Pattern Recognition. The present invention presents a process by which a user can recognize an unknown sting X, which is an element of a finite, but possibly larger Dictionary, H, by processing the information contained in its noisy/inexact version, Y, where Y is assumed to contain substitution, insertion or deletion errors. The recognized string, which is the best estimate X+ of X, is defined as that element of H which minimizes the Generalized Levenshtein Distance D(X,Y) between X and Y, for all X<H. Rather than evaluate D(X,Y) for every X<H sequentially, the present invention achieves this simultaneously for every X<H by representing the Dictionary as a Trie, and searching the Trie using a new AI-based search strategy.