Customised OCR Correction for Historical Medical Text

Abstract

Historical text archives constitute a rich and diverse source of information, which is becoming increasingly readily accessible, owing to large-scale digitisation efforts. Searchable access is typically provided by applying Optical Character Recognition (OCR) software to scanned page images. Often, however, the automatically recognised text contains a large number of errors, since OCR systems are typically optimised to deal with modern documents, and can struggle with historical document features, including variable print characteristics and archaic vocabulary usage. Low quality OCR text can reduce the efficiency of search systems over historical archives, particularly semantic systems that are based on the application of sophisticated text mining (TM) techniques. We report on a new OCR correction strategy, customised for historical medical documents. The method combines rule-based correction of regular errors with a medically-tuned spellchecking strategy, whose corrections are guided by information about subject-specific language usage from the publication period of the article to be corrected. The performance of our method compares favourably to other OCR post-correction strategies, in improving word-level accuracy of poor-quality documents by up to 16%.