Old newspapers provide a window into our past, and a new algorithm co-developed by a University at Buffalo School of Management researcher is helping turn those historic documents into useful, searchable data.
Published in Decision Support Systems, the algorithm can find and rank people’s names in order of importance from the results produced by optical character recognition (OCR), the computerized method of converting scanned documents into text that is often messy.
“It’s a known fact that when OCR software is run, very often the text gets garbled,” says Haimonti Dutta, Ph.D., assistant professor of management science and systems in the UB School of Management. “With old newspapers, books and magazines, problems can arise from poor ink quality, crumpled or torn paper, or even unusual page layouts the software isn’t expecting.”
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