Pattern matching techniques for correcting low-confidence OCR words in a known context

Glenn Ford; Susan E. Hauser; Daniel X. Le; George R. Thoma

doi:10.1117/12.410842

21 December 2000 Pattern matching techniques for correcting low-confidence OCR words in a known context

Glenn Ford, Susan E. Hauser, Daniel X. Le, George R. Thoma

Proceedings Volume 4307, Document Recognition and Retrieval VIII; (2000) https://doi.org/10.1117/12.410842
Event: Photonics West 2001 - Electronic Imaging, 2001, San Jose, CA, United States

Abstract

A commercial OCR system is a key component of a system developed at the National Library of Medicine for the automated extraction of bibliographic fields from biomedical journals. This 5-engine OCR system, while exhibiting high performance overall, does not reliably convert very small characters, especially those that are in italics. As a result, the 'affiliations' field that typically contains such characters in most journals, is not captured accurately, and requires a disproportionately high manual input. To correct this problem, dictionaries have been created from words occurring in this field (e.g., university, department, street addresses, names of cities, etc.) from 230,000 articles already processed. The OCR output corresponding to the affiliation field is then matched against these dictionary entries by approximate string-matching techniques, and the ranked matches are presented to operators for verification. This paper outlines the techniques employed and the results of a comparative evaluation.

Citation Download Citation

Glenn Ford, Susan E. Hauser, Daniel X. Le, and George R. Thoma "Pattern matching techniques for correcting low-confidence OCR words in a known context", Proc. SPIE 4307, Document Recognition and Retrieval VIII, (21 December 2000); https://doi.org/10.1117/12.410842

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