OCR ERRORS & NAMED ENTITY

RECOGNITION IN LA STAMPA’S HISTORICAL ARCHIVE

Andrea Bolioli, Eleonora Marchioni, Raffaella Ventaglio

abolioli@celi.it, marchioni@celi.it, ventaglio@celi.it

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Microfilm Scan and OCR Full text indexing NER and infographics

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The project was realized in 2010-2011. Public web site: www.archiviolastampa.it

5 million newspaper articles, 1910-2005

We annotated about 16,000 errors and corrections in a sample of 894 front page articles. A few examples: dustin hoflman, hoftman, holfman, hollman, hotfman, hotlman (dustin hoffmann) , pohtica (politica), de (dc), pei (pci), doc um e nto (documento) , re- latore (relatore), ima (una), gh (gli). OCR errors classified in types: Segmentation, Hyphenation, Character misrecognition, Punctuation, Graphics, etc.

Type and Percentage of errors ( errors / num of tokens )

OCR Errors in the Historical Archive

People recognized in the front pages of the newspaper (number of articles).

Measures over a test corpus of 500 documents.

NEs that occur more than 10 times, extracted from 4,8M documents.

Named Entity Recognition