KEYWORDS: Optical character recognition, Information science, Scientific research, Lanthanum, Data storage, Data processing, Internet, Electronic imaging, Databases, Feature extraction
We report on an attempt to build an automatic redaction system by applying information extraction techniques to the identification of private dates of birth. We conclude that automatic redaction is a promising concept although information extraction is significantly affected by the presence of OCR error.
This paper presents the implementation and evaluation of a Hidden Markov Model to extract addresses from OCR text. Although Hidden Markov Models discover addresses with high precision and recall, this type of Information Extraction task seems to be affected negatively by the presence of OCR text.
Hundreds of experiments over the last decade on the retrieval of OCR documents performed by the Information Science Research Institute have shown that OCR errors do not significantly affect retrievability. We extend those results to show that in the case of proximity searching, the removal of running headers and footers from OCR text will not improve retrievability for such searches.
A rule-based automatic text categorizer was tested to see if two types of thesaurus expansion, called query expansion and Junker expansion respectively, would improve categorization. Thesauri used were domain-specific to an OCR test collection focussed on a single topic. Results show that neither type of expansion significantly improved categorization.
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