Unconstrained 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, we examine the influence of the vocabulary size, the number of training samples, and the number of classifiers on the performance of three ensemble methods in the context of cursive handwriting recognition. All experiments were conducted using an off-line handwritten word recognizer based on hidden Markov models (HMMs). (c) 2004 Elsevier Ltd. All rights reserved.
