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%.


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    Title :

    Confidence-scoring post-processing for off-line handwritten-character recognition verification


    Contributors:


    Publication date :

    2003-01-01


    Size :

    376209 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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