Abstract
This work investigates three different rejection strategies for offline handwritten sentence recognition. The rejection strategies are implemented as a postprocessing step of a hidden Markov model based text recognition system and are based on confidence measures derived from a list of candidate sentences produced by the recognizer. The better performing confidence measures make use of the fact that the recognizer integrates a word bigram language model. Experimental results on extracted sentences from the IAM database validate the effectiveness of the proposed rejection strategies.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
