We apply confidence-scoring techniques to verify the output of a handwriting recognizer. We evaluate a variety of scoring functions, including likelihood ratios and estimated posterior probabilities of correctness, in a postprocessing mode to generate confidence scores at the character or word level. Using the post-processor in conjunction with an HMM-based on-line handwriting recognizer for large-vocabulary word recognition, receiver-operating-characteristic (ROC) curves reveal that our post-processor is able to reject correctly 90% of recognizer errors while only falsely rejecting 33% of correctly-recognized words. For isolated-digit recognition, we achieve a correct rejection rate of 90% while keeping false rejection down to 13%.


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    Title :

    Confidence modeling for verification post-processing for handwriting recognition


    Contributors:


    Publication date :

    2002-01-01


    Size :

    444479 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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