  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
they can be conditioned upon, allowing further inference to be done
efficiently. Specifically, 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 training data. While many current OCR
systems produce measures of confidence for the identity of each letter or word,
thresholding these confidence values, even at very 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 probabilities, 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 words, even in noisy documents, of which we are highly confident. On
our set of 10 documents, 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.
