A flexible elastic control plane for private clouds
Upendra Sharma, Prashant Shenoy, et al.
ICCAC 2013
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.
Upendra Sharma, Prashant Shenoy, et al.
ICCAC 2013
Hans-Werner Fink, Heinz Schmid, et al.
Journal of the Optical Society of America A: Optics and Image Science, and Vision
Holly Rushmeier, J. Gomes, et al.
3DIM 2003
Trieu C. Chieu, Shiwa S. Fu, et al.
ICEC 2003