Abstract
This paper investigates the use of multiple classifier methods for offline handwritten text line recognition. To obtain ensembles of recognisers we implement a random feature subspace method. The word sequences returned by the individual ensemble members are first aligned. Then the final word sequence is produced. For this purpose we use a voting method and two novel statistical combination methods. The conducted experiments show that the proposed multiple classifier methods have the potential to improve the recognition accuracy of single recognisers.
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Bertolami, R., Bunke, H. (2007). Multiple Classifier Methods for Offline Handwritten Text Line Recognition. In: Haindl, M., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2007. Lecture Notes in Computer Science, vol 4472. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72523-7_8
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DOI: https://doi.org/10.1007/978-3-540-72523-7_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72481-0
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