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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    Titel :

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


    Beteiligte:
    Pitrelli, J.F. (Autor:in) / Perrone, M.P. (Autor:in)


    Erscheinungsdatum :

    2003-01-01


    Format / Umfang :

    376209 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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