Abstract:Historical documents are typically digitized using optical Character Recognition. While effective, the results may not always be accurate and are highly dependent on the input. Consequently, degraded documents are often corrupted. Our focus is finding flexible, reliable methods to correct for such degradation, in the face of limited resources. We extend upon our substring and context fusion based retrieval system known as Segments, to consider metadata. By extracting topics from documents, and supplementing an… Show more
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