Generalized hidden Markov models - Part II: Application to handwritten word recognition

This is the second paper in a series of two papers describing a novel appro ach for generalizing classical hidden Markov models using fuzzy measures an d fuzzy integrals and their application to the problem of handwritten word recognition. This paper presents an application of the generalized hidden M arkov models to handwritten word recognition. The system represents a word image as an ordered list of observation vectors by encoding features comput ed from each column in the given word image. Word models are formed by conc atenating the state chains of the constituent character hidden Markov model s. The novel work presented includes the preprocessing? feature extraction, and the application of the generalized hidden Markov models to handwritten word recognition. Methods for training the classical and generalized (fuzz y) models are described. Experiments mere performed on a standard data set of handwritten nord images obtained from the U. S. Post Office mail stream, which contains real-word samples of different styles and qualities.