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). 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.
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