Rejection Strategies for Offline Handwritten Sentence Recognition

This paper 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.

• Type: Conference
• Year: 2004
• Where: ICPR
• Authors: Horst Bunke, Matthias Zimmermann, Roman Bertolami
• Keywords: Better Performing Confidence, Computer Vision, Confidence Measures, Handwritten Sentence Recognition, ICPR 2004