Model-based restoration of document images for OCR

This paper presents a methodology for model based restoration of degraded document imagery. The methodology has the advantages of being able to adapt to nonuniform page degradations and of being based on a model of image defects that is estimated directly from a set of calibrating degraded document images. Further, unlike other global filtering schemes, our methodology filters only words that have been misspelled by the OCR with a high probability. In the first stage of the process, we extract a training sample of candidate misspelled word subimages from the set of calibration images before and after the degradation that we wish to undo. These word subimages are registered to extract defect pixels. The second stage of our methodology uses a vector quantization based algorithm to construct a summary model of the defect pixels. The final stage of the algorithm uses the summary model to restore degraded document images. We evaluate the performance of the methodology for a variety of parameter settings on a real world sample of degraded FAX transmitted documents. The methodology eliminates up to 56.4% of the OCR character errors introduced as a result of FAX transmission for our sample experiment.