Ensemble methods to improve the performance of an English handwritten text line recognizer

Authors:
Roman Bertolami;Horst Bunke
Affiliations:
Institute of Computer Science and Applied Mathematics, University of Bern, Bern, Switzerland;Institute of Computer Science and Applied Mathematics, University of Bern, Bern, Switzerland
Venue:
SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
Year:
2006

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Abstract

This paper describes recent work on ensemble methods for offline handwritten text line recognition. We discuss techniques to build ensembles of recognizers by systematically altering the training data or the system architecture. To combine the results of the ensemble members, we propose to apply ROVER, a voting based framework commonly used in continuous speech recognition. Additionally, we extend this framework with a statistical combination method. The experimental evaluation shows that the proposed ensemble methods have the potential to improve the recognition accuracy compared to a single recognizer.