We present a document-specific OCR system and apply it to a corpus of fixed business letters. Unsupervised classification of the segmented character bitmaps on each page, using a "clump" metric, typically yields several hundred clusters with highly skewed populations. Letter identities are assigned to each cluster by maximizing matches with a lexicon of English words. We found that for 2/3 of the pages, we can identify almost 80% of the words included in the lexicon, without any shape training. Residual errors are caused by mis-segmentation including missed lines and punctuation. This research differs from earlier attempts to apply cipher decoding to OCR in: (1) using real data; (2) a more appropriate clustering algorithm; and (3) decoding a many-to-many instead of a one-to-one mapping between clusters and letters.