Ensembles of classifiers for handwritten word recognition

Handwritten text recognition is one of the most difficult problems in the field of pattern recognition. Recently, a number of classifier creation and combination methods, known as ensemble methods, have been proposed in the field of machine learning. They have shown improved recognition performance over single classifiers. In this paper the application of some of those ensemble methods in the domain of offline cursive handwritten word recognition is described. The basic word recognizers are given by hidden Markov models (HMMs). It is demonstrated through experiments that ensemble methods have the potential of improving recognition accuracy also in the domain of handwriting recognition.