Accuracy of Digitization

The Update on February 2012 Activities of the HathiTrust reports on research being done by the HathiTrust Research Center (HTRC) to quantify occurrences of Optical Character Recognition (OCR) errors in the HathiTrust corpus. OCR is the technology that converts a scanned image to text that can be searched and analyzed. Members of the HTRC examined 256,000 non-Google digitized volumes from HathiTrust using a clever algorithm that compared OCR text to a dictionary of known words. Using a supercomputer and a set of rules that were verified by a human expert, they found that 84.9 percent of the volumes examined (217,754 of the 256,416) had one or more OCR errors and 11% of the pages (7,745,034 of the 69,297,000) had one or more errors. The average number of errors per volume was 156.

As we at FGI have argued here before, we believe that it essential to take into account OCR accuracy and error rates when digitizing paper collections. This is particularly important when digitizing books that contain statistical tables since it is harder to use current OCR technologies to accurately convert image scans to numbers than it is to convert scans to text. It is also harder to evaluate the accuracy of such conversions; you can’t use a dictionary of known statistics the way you can use a dictionary of known words. (The HTRC study did not, apparently, examine accuracy of statistical table conversions.) Because of the large volume of such information in government publications, this is a very important issue as we collectively try to digitize our paper collections, evaluate their accuracy and usability, and determine how many paper copies we need to keep after digitization (see Achieving a collaborative FDLP future).

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