Rapid best first retrieval from massive dictionaries with poorly segmented inputs

The author describes recent extensions to his rapid retrieval system. The initial system was developed to perform best-first retrieval from massive dictionaries given uncertain inputs, and has since been extended to provide a complete and uniform method for incorporating most kinds of contextual knowledge in the pattern recognition process. The modification described is a significant one, whereby the input to the system is now given as a directed graph, rather than a straight sequence of character hypothesis sets. The original formulation only dealt with substitution errors. The extended version copes with insertion and deletion errors, and any number of alternative segmentations of the input, as would naturally arise in cursive script or due to touching printed characters. This research builds on the syntactic neural network (SNN) rapid dictionary search system which is briefly described