Rejection strategies for offline handwritten text line recognition

Authors:
Roman Bertolami;Matthias Zimmermann;Horst Bunke
Affiliations:
Institute of Computer Science and Applied Mathematics, University of Bern, Bern, Switzerland;International Computer Science Institute, Berkeley and Institute of Computer Science and Applied Mathematics, University of Bern, Bern, Switzerland;Institute of Computer Science and Applied Mathematics, University of Bern, Neubrückstrasse, Bern, Switzerland
Venue:
Pattern Recognition Letters
Year:
2006

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Abstract

This paper investigates rejection strategies for unconstrained offline handwritten text line recognition. The rejection strategies depend on various confidence measures that are based on alternative word sequences. The alternative word sequences are derived from specific integration of a statistical language model in the hidden Markov model based recognition system. Extensive experiments on the IAM database validate the proposed schemes and show that the novel confidence measures clearly outperform two baseline systems which use normalised likelihoods and local n-best lists, respectively.