Confidence-Scoring Post-Processing for Off-Line Handwritten-Character Recognition Verification

Authors:
John F. Pitrelli;Michael P. Perrone
Affiliations:
-;-
Venue:
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Year:
2003

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Abstract

We apply confidence-scoring techniques to verify the outputof an off-line handwritten-character recognizer. Weevaluate a variety of scoring functions, including likelihoodratios 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-basedrecognizer, on mixed-case letters, receiver-operating-characteristic(ROC) curves reveal that our post-processoris able to reject correctly 90% of recognizer errors whileonly falsely rejecting 18.6% of correctly-recognized letters.For isolated-digit recognition, we achieve a correct rejectionrate of 95% while keeping false rejection down to 8.7%.