An on-line handwriting recognition system using Fisher segmental matching and Hypotheses Propagation Network

We propose an on-line handwriting recognition approach that integrates local bottom-up constructs with a global top-down measure into a modular recognition engine. The bottom-up process uses local point features for hypothesizing character segmentations and the top-down part performs shape matching for evaluating the segmentations. The shape comparison, called Fisher segmental matching, is based on Fisher´s linear discriminant analysis. Along with an efficient ligature modeling, the segmentations and their matching scores are integrated into a recognition engine termed Hypotheses Propagation Network, which runs a variant of the topological sort algorithm of graph search. The result is a system that is more shape-oriented less dependent on local and temporal features, modular in construction and has a rich range of opportunities for further extensions. Our system currently performs at 95% of recognition rate on cursive scripts with a 460 word dictionary