Performing information extraction to improve OCR error detection in semi-structured historical documents

Optical character recognition (OCR) produces transcriptions of document images. These transcriptions often contain incorrectly recognized characters which we must avoid or correct downstream. An ability to both identify OCR errors and extract information from OCR output would allow us to extract and index only correct information and to post-process specific parts of the OCR output with targeted resources (e.g. re-OCR using specialized dictionaries). We present a general approach to OCR error detection that uses a hidden Markov model trained to simultaneously detect OCR errors and extract information. We evaluate this approach in two information extraction settings and on semi-structured text from two machine-printed family history documents. We show this joint approach to OCR error detection to be an improvement over two alternative approaches, one based on dictionary matching and the other using a hidden Markov model trained only to detect OCR errors. In particular, we report an average of 8% increase in macro-averaged F-measure between the dictionary approach and our best HMM. Our contribution is to show how an OCR error detection approach based on a word model can be improved by joining this task with an information extraction task, and that an improvement in OCR error detection is achieved regardless of the information extraction task.