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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Bertolami, R., & Bunke, H. (2007). Multiple classifier methods for offline handwritten text line recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4472 LNCS, pp. 72–81). Springer Verlag. https://doi.org/10.1007/978-3-540-72523-7_8
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