Multiple Classifier Methods for Offline Handwritten Text Line Recognition

  • Roman Bertolami
  • Horst Bunke
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4472)


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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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Roman Bertolami
    • 1
  • Horst Bunke
    • 1
  1. 1.Institute of Computer Science and Applied Mathematics, University of Bern, Neubrückstrasse 10, CH-3012 BernSwitzerland

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