Yang Wang, Zicheng Liu, et al.
CVPR 2007
Confidence scoring can assist in determining how to use imperfect handwriting-recognition output. We explore a confidence-scoring framework for post-processing recognition for two purposes: Deciding when to reject the recognizer's output, and detecting when to change recognition parameters e.g., to relax a word-set constraint. Varied confidence scores, including likelihood ratios and posterior probabilities, are applied to an Hidden-Markov-Model (HMM) based on-line recognizer. Receiver-operating characteristic curves reveal that we successfully reject 90% of word recognition errors while rejecting only 33% of correctly-recognized words. For isolated digit recognition, we achieve 90% correct rejection while limiting false rejection to 13%. © Springer-Verlag 2005.
Yang Wang, Zicheng Liu, et al.
CVPR 2007
Kuan-Yu Chen, Shih-Hung Liu, et al.
EMNLP 2014
Jun Xie, Rogerio Schmidt Feris, et al.
ICIP 2014
Milind R. Naphade, Sankar Basu, et al.
ICPR 2008