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Although large language models are used in speech recognition and machine translation applications, OCR systems are "far behind" in their use of language models. The reason for this is not the laggardness of the OCR community, but the fact that, at high accuracies, a frequency-based language model can do more damage than good, unless carefully applied. This paper presents an analysis of this discrepancy with the help of the Google Books n-gram Corpus, and concludes that noisy-channel models that closely model the underlying classifier and segmentation errors are required.
Ray Smith (Thu,) studied this question.