Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
536877 | Pattern Recognition Letters | 2012 | 8 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Roman Bertolami, Matthias Zimmermann, Horst Bunke,