  Commercial OCR packages work best with high-quality scanned images. They
often produce poor results when the image is degraded, either because the
original itself was poor quality, or because of excessive photocopying. The
ability to predict the word failure rate of OCR from a statistical analysis of
the image can help in making decisions in the trade-off between the success
rate of OCR and the cost of human correction of errors. This paper describes an
investigation of OCR of degraded text images using a standard OCR engine (Adobe
Capture). The documents were selected from those in the archive at Los Alamos
National Laboratory. By introducing noise in a controlled manner into perfect
documents, we show how the quality of OCR can be predicted from the nature of
the noise. The preliminary results show that a simple noise model can give good
prediction of the number of OCR errors.
