We apply confidence-scoring techniques to verify the output of an off-line handwritten-character recognizer. We evaluate a variety of scoring functions, including likelihood ratios and estimated posterior probabilities of correctness, in a post-processing mode, to generate confidence scores. Using the post-processor in conjunction with a neural-net-based recognizer, on mixed-case letters, receiver-operating-characteristic (ROC) curves reveal that our post-processor is able to reject correctly 90% of recognizer errors while only falsely rejecting 18.6% of correctly-recognized letters. For isolated-digit recognition, we achieve a correct rejection rate of 95% while keeping false rejection down to 8.7%.
Confidence-scoring post-processing for off-line handwritten-character recognition verification
2003-01-01
376209 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Confidence-Scoring Post-Processing for Off-Line Handwritten-Character Recognition Verification
British Library Conference Proceedings | 2003
|Confidence Modeling for Verification Post-Processing for Handwriting Recognition
British Library Conference Proceedings | 2002
|A Flexible Recognition Engine for Complex On-line Handwritten Character Recognition
British Library Conference Proceedings | 2003
|