Normalisation of confidence voting methods applied to a fast handwritten OCR classification

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Title: Normalisation of confidence voting methods applied to a fast handwritten OCR classification
Authors: Rico Juan, Juan Ramón | Iñesta, José M.
Research Group/s: Reconocimiento de Formas e Inteligencia Artificial
Center, Department or Service: Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos
Keywords: Ensemble classifiers | Handwritting recognition
Knowledge Area: Lenguajes y Sistemas Informáticos
Date Created: 2007
Issue Date: 2008
Publisher: Springer Berlin / Heidelberg
Citation: RICO JUAN, Juan Ramón; IÑESTA QUEREDA, José Manuel. "Normalisation of confidence voting methods applied to a fast handwritten OCR classification". En: Computer Recognition Systems 2. Berlin : Springer, 2008. (Advances in Soft Computing; 45/2008). ISBN 978-3-540-75174-8, pp. 405-412
Abstract: In this work, a normalisation of the weights utilized for combining classifiers decisions based on similarity Euclidean distance is presented. This normalisation is used by the confidence voting methods to decrease the final error rate in an OCR task. Different features from the characters are extracted. Each set of features is processed by a single classifier and then the decisions of the individual classifiers are combined using weighted votes, using different techniques. The error rates obtained are as good or slightly better than those obtained using a Freeman chain codes as contour representation and the string edit distance as similarity measure, but the complexity and classification time decrease dramatically.
Sponsor: Work supported by the Spanish CICYT under project TIN2006-14932-C02, partially supported by EU ERDF and Generalitat Valenciana I+D+i under project GV06/166.
ISBN: 978-3-540-75174-8
ISSN: 1615-3871 (Print) | 1860-0794 (Online)
DOI: 10.1007/978-3-540-75175-5_51
Language: eng
Type: info:eu-repo/semantics/bookPart
Rights: The original publication is available at
Peer Review: si
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