Newspaper collections are the subject of an increasing number of large-scale digitisation projects. In Papers Past (http://paperspast.natlib.govt.nz), a collection of over a million newspaper pages, the introduction of full-text search has made a wealth of information findable that was previously hidden. The search feature is dependent on text extracted from the newspaper page images with Optical Character Recognition (OCR), so any improvement in OCR accuracy will add value to the collection by improving our users' chances of finding useful information.
This article details the work undertaken by the National Library of Australia Newspaper Digitisation Program on identifying and testing solutions to improve OCR accuracy in large scale newspaper digitisation programs. In 2007 and 2008 several different solutions were identified, applied and tested on digitised material now available in the Australian Newspapers Digitisation Program beta service <http://ndpbeta.nla.gov.au/ndp/del/home>. This article gives a state of the art overview of how OCR software works on newspapers, factors that effect OCR accuracy, methods of measuring accuracy, methods of improving accuracy, and testing methods and results for specific solutions that were considered viable for large scale text digitisation projects.
Large quantities of historical newspapers are being digitized and OCRd. We describe a framework for processing the OCRd text to identify articles and extract metadata for them. We describe the article schema and provide examples of features that facilitate automatic indexing of them. For this processing, we employ lexical semantics, structural models, and community content. Furthermore, we describe visualization and summarization techniques that can be used to present the extracted events.
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