Quality assessment and restoration of typewritten document images

Abstract.

We present a useful method for assessing the quality of a typewritten document image and automatically selecting an optimal restoration method based on that assessment. We use five quality measures that assess the severity of background speckle, touching characters, and broken characters. A linear classifier uses these measures to select a restoration method. On a 139-document corpus, our methodology reduced the corpus OCR character error rate from 20.27% to 12.60%.