Generalized hidden Markov models. II. Application to handwritten word recognition

This is the second paper in a series of two papers describing a novel approach for generalizing classical hidden Markov models using fuzzy measures and fuzzy integrals and their application to the problem of handwritten word recognition. This paper presents an application of the generalized hidden Markov models to handwritten word recognition. The system represents a word image as an ordered list of observation vectors by encoding features computed from each column in the given word image. Word models are formed by concatenating the state chains of the constituent character hidden Markov models. 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 (fuzzy) models are described. Experiments were performed on a standard data set of handwritten word images obtained from the U. S. Post Office mail stream, which contains real-word samples of different styles and qualities.