Non-cumulative character scoring in a forward search for online handwriting recognition

In this paper, we describe a novel and efficient search strategy as it is applied to on-line handwriting recognition. The recognition system, which is based on Viterbi decoding, computes posterior probabilities of characters based on sequences of observations, referred to as segments. Posterior distributions of segments are appealing because they allow correlation modeling of the entire segment. In addition, they provide a reliable confidence-rejection mechanism due to their discriminative nature. These posterior scores, which we term non-cumulative, cannot be used for hypothesis pruning as it is done in a standard time synchronous beam search. We propose an organization of the search algorithm that addresses the difficulties posed by the use of non-cumulative character scoring in an efficient way. We report on a writer-independent recognition system that achieves a tenfold reduction in the required number of theories while maintaining the same level of accuracy. Finally, we provide a comparison between the segmental posterior probabilities and the frame-based, class-conditional distributions of the traditional HMM approach that highlights the differences in the search methodology