Thomas L. Packer
ABSTRACT
Named entity recognition applied to scanned and OCRed historical documents can contribute to the discoverability of historical information. However, entity recognition from some historical corpora is much more difficult than from natively digital text because of the marked presence of word errors and absence of page layout information. How difficult can it be and what level of quality can be expected? We apply three typical extraction algorithms to the task of extracting person names from multiple types of noisy OCR documents found in the collection of a major genealogy content provider and compare their performance using a number of quality metrics. We also show an improvement in extraction quality using a majority-vote ensemble of the three extractors. We evaluate the extraction quality with respect to two references: what a human can manually extract from OCR output and from the original document images. We illustrate the challenges and opportunities at hand for extracting names from OCRed data and identify directions for further improvement.
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Index Terms
- Extracting person names from diverse and noisy OCR text
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Reviews
Joao Luis Garcia Rosa
The authors of this paper provide a satisfying read about name entity recognition (NER) in noisy optical character recognition (OCR) texts. They deliver on their promise of providing answers to many questions that researchers in this area might have. Packer et al. draw many interesting conclusions about performing the difficult task of extracting names from noisy scanned documents: "Word order errors can play a bigger role in poor extraction performance than character recognition errors"; "The knowledge-based approaches performed better than the machine learning (ML) approaches"; and "Combining basic extraction methods can produce higher quality NER." Regarding the conclusion about machine learning approaches, ML lovers need not despair. The authors point out two ways to overcome their deficiencies: either apply a more realistic noise model of OCR errors to the computational natural language learning (CoNLL) training data or use semi-supervised ML techniques to take advantage of the large number of unlabeled documents. Online Computing Reviews Service
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