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Optimization of Weights in a Multiple Classifier Handwritten Word Recognition System Using a Genetic Algorithm
Günter, Simon (University of Bern (Suïssa). Department of Computer Science)
Bunke, Horst (University of Bern (Suïssa). Department of Computer Science)

Date: 2004
Abstract: Automatic handwritten text recognition by computer has a number of interesting applications. However, due to a great variety of individual writing styles, the problem is very difficult and far from being solved. Recently, a number of classifier creation methods, known as ensemble methods, have been proposed in the field of machine learning. They have shown improved recognition performance over single classifiers. For the combination of these classifiers many methods have been proposed in the literature. In this paper we describe a weighted voting scheme where the weights are obtained by a genetic algorithm.
Rights: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Language: Anglès.
Document: article ; recerca ; publishedVersion
Subject: Handwritten Text Recognition ; Classifier Combination ; Hidden Markov Models (HMM) ; Weighted Voting ; Genetic Algorithm ; Reconeixement de text manuscrit ; Combinació classificadora ; Models Markov amagats ; Votació ponderada ; Algoritme genètic ; Reconocimiento de texto manuscrito ; Combinación clasificadora ; Modelos Markov escondidos ; Votación ponderada ; Algoritmo genético
Published in: ELCVIA : Electronic Letters on Computer Vision and Image Analysis, V. 3 n. 1 (2004) p. 25-44, ISSN 1577-5097

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DOI: 10.5565/rev/elcvia.67

17 p, 165.9 KB

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 Record created 2008-03-11, last modified 2018-11-13

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