Abstract
This paper introduces a general methodology for detecting and reducing the errors in a handwriting recognition task. The methodology is based on confidence modeling and its main difference is the use of two parallel classifiers for error assessment. The experimental benchmark associated with this approach is described as well as exhaustive results are provided for two real world recognizers on a large database.
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Rodríguez, J.A., Sánchez, G., Lladós, J. (2007). Rejection Strategies Involving Classifier Combination for Handwriting Recognition. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72849-8_13
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DOI: https://doi.org/10.1007/978-3-540-72849-8_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72848-1
Online ISBN: 978-3-540-72849-8
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