metadata dates that were more than 5 years from the date determined by a human examining the book. Because errors are much more common among older books, and because the actual corpora are strongly biased toward recent works, the likelihood of error in a randomly sampled book from the final corpus is much lower than 6.2%. As a point of comparison, 27 of 100 books (27%) selected at random from an unfiltered corpus contained date-of-publication errors of greater than 5 years. The unfiltered corpus was created using a sampling strategy similar to that of Eng-1M. This selection mechanism favored recent books (which are more frequent) and pre-1800 books, which were excluded in the sampling strategy for filtered books; as such the two numbers (6.2% and 27%) give a sense of the improvement, but are not strictly comparable. Note that since the base corpora were generated (August 2009), many additional improvements have been made to the metadata dates used in Google Book Search itself. As such, these numbers do not reflect the accuracy of the Google Book Search online tool. II.1B. OCR quality The challenge of performing accurate OCR on the entire books dataset is compounded by variations in such factors as language, font, size, legibility, and physical condition of the book. OCR quality was assessed using an algorithm developed by Popat et al. (Ref S3). This algorithm yields a probability that expresses the confidence that a given sequence of text generated by OCR is correct. Incorrect or anomalous text can result from gross imperfections in the scanned images, or as a result of markings or drawings. This algorithm uses sophisticated statistics, a variant of the Partial by Partial Matching (PPM) model, to compute for each glyph (character) the probability that it is anomalous given other nearby glyphs. (‘Nearby' refers to 2-dimensional distance on the original scanned image, hence glyphs above, below, to the left, and to the right of the target glyph.) The model parameters a