Probabilistic Automaton Model for Fuzzy English-Text Retrieval
Optical character reader (OCR) misrecognition is a serious problem when searching against OCR-scanned documents in databases such as digital libraries. This paper proposes fuzzy retrieval methods for English text that contains errors in the recognized text without cor- recting the errors manually. Costs are thereby reduced. The proposed methods generate multiple search terms for each input query term based on probabilistic automata reflecting both error-occurrence probabilities and character-connection probabilities. Experimental results of test-set retrieval indicate that one of the proposed methods improves the recall rate from 95.56% to 97.88% at the cost of a decrease in precision rate from 100.00% to 95.52% with 20 expanded search terms.
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