An On-Line Handwriting Recognizer with Fisher Matching, Hypotheses Propagation Network and Context Constraint Models
We have developed an on-line handwriting recognition system. Our approach 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. The component character recognizer of the system uses two kinds of Fisher matching based on different representations and combines the information to form the multiple experts paradigm.
Along with an efficient ligature modeling, the segmentations and their character recognition scores are integrated into a recognition engine termed Hypotheses Propagation Network (HPN), which runs a variant of topological sort algorithm of graph search. The HPN improves on the conventional Hidden Markov Model and the Viterbi search by using the more robust mean-based scores for word level hypotheses and keeping multiple predecessors during the search.
We have also studied and implemented a geometric context modeling termed Visual Bigram Modeling that improves the accuracy of the system's performance by taking the geometric constraints into account, in which the component characters in a word can be formed in relation with the neighboring characters. The result is a shape-oriented system, robust with respect to local and temporal features, modular in construction and has a rich range of opportunities for further extensions.