We consider models for which it is important, early in processing, to estimate some variables with high precision, but perhaps at relatively low rates of recall. If some variables can be identified with near certainty, then th ey can be conditioned upon, allowing further inference to be done efficiently. Spe cifically, we consider optical character recognition (OCR) systems that can be bootstrapped by identifying a subset of correctly translated document words with very high precision. This “clean set” is subsequently used as document-specific train ing data. While many current OCR systems produce measures of confidence for the id entity of each letter or word, thresholding these confidence values, even at ve ry high values, still produces some errors. We introduce a novel technique for identifying a set of correct words with very high precision. Rather than estimating posterior probabil ities, we bound the probability that any given word is incorrect under very general assumptions, using an approximate worst case analysis. As a result, the parameters of the model are nearly irrelevant, and we are able to identify a subset of wor ds, even in noisy documents, of which we are highly confident. On our set of 10 docum ents, we are able to identify about 6% of the words on average without making a single error. This ability to produce word lists with very high precision allows us to use a family of models which depends upon such clean word lists.
['Andrew Kae', 'Gary B. Huang', 'Erik G. Learned-Miller']
Bounding the Probability of Error for High Precision Recognition