MULTIPLE CLASSIFIER SYSTEMS IN OFFLINE HANDWRITTEN WORD RECOGNITION — ON THE INFLUENCE OF TRAINING SET AND VOCABULARY SIZE

Abstract

Handwritten text recognition is one of the most difficult problems in the field of pattern recognition. Recently, a number of classifier creation methods, known as ensemble methods, have been proposed in the field of machine learning. It has been shown that these methods are able to substantially improve recognition performance in complex classification tasks. In this paper we examine the influence of the vocabulary size and the number of training samples on the performance of three ensemble methods in the context of handwritten word recognition. The experiments were conducted with two different offline hidden Markov model based handwritten word recognizers.