A Stochastic Model Combining Discrete Symbols and Continuous Attributes and Its Application to Handwriting Recognition

This paper introduces a new stochastic framework of modeling sequences of features that are combinations of discrete symbols and continuous attributes. Unlike traditional hidden Markov models, the new model emits observations on transitions instead of states. In this framework, a feature is first labeled with a symbol and then a set of featuredependent continuous attributes is associated to give more details of the feature. This two-level hierarchy is modeled by symbol observation probabilities which are discrete and attribute observation probabilities which are continuous. The model is rigorously defined and the algorithms for its training and decoding are presented. This framework has been applied to off-line handwritten word recognition using high-level structural features and proves its effectiveness in experiments.