Stochastic Modeling of High-level Structures in Handwritten Word Recognition

Handwritten word recognition is an important topic in pattern recognition. It has many applications in automated document processing such as postal address interpretation, bankcheck reading and form reading. There is evidence from psychological studies that word shape plays a significant role in human visual word recognition. High-level structures in handwriting, such as loops, junctions, turns, and ends, are considered to be highly shape-defining. These structures can be more precisely described by their attributes such as position, orientation, curvature, and size. Algorithms based on skeletal graphs are designed to extract structural features. Viewing handwriting as a sequence of structural features, we choose stochastic finite-state automata (SFSAs) as our modeling tool. We extend SFSAs to model high-level structures and their continuous attributes, and view the popular hidden Markov models (HMMs) as special cases of SFSAs obtained by tying parameters on transitions. Experimental results on these two modeling tools have shown advantages of SFSAs over HMMs. To allow real-time applications of the stochastic word recognizers, we introduce several fast-decoding techniques, including character-level dynamic programming, duration constraint, prefix/suffix sharing, choice pruning, etc. A parallel version of the recognizer is also implemented by splitting large lexicons. The resulting word recognizer is better than or comparable to other recognizers in terms of recognition accuracy and speed. For recognizers building word recognition on character recognition, we propose a performance model to associate word recognition accuracy with character recognition accuracy. The model parameters can be determined by multiple regression on accuracy rates obtained on the training data. This model can be used to predict a recognizer’s performance given a lexicon and promises its applications in dynamic classifier selection and combination.