Syntactic pattern classification by branch and bound search

A methodology for classifying syntactic patterns that performs a branch-and-bound search over a set of prototypes is proposed. The prototypes are first clustered hierarchically and the search is performed over the hierarchy. The proposed technique is applied to a pattern recognition system in which images are described by the sequence of features extracted from the chain codes of their contours. A rotationally invariant string distance measure is defined that compares two feature strings. The methodology discussed is compared to a nearest neighbor classifier that uses 12000 prototypes. The proposed technique decreases the time required to recognize a pattern by 93% and maintains a recognition rate of greater than 90%