Off-line Cursive Handwriting Recognition-On the Influence of Training Set and Vocabulary Size in Multiple Classifier Systems

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. 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 cursive handwriting recognition. All experiments were conducted using an off-line HMM-based handwritten word recognizer.

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