Results of applying probabilistic IR to OCR text
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluation of model-based retrieval effectiveness with OCR text
ACM Transactions on Information Systems (TOIS)
Effects of OCR errors on ranking and feedback using the vector space model
Information Processing and Management: an International Journal
Reduction of Expanded Search Terms for Fuzzy English-Text Retrieval
ECDL '98 Proceedings of the Second European Conference on Research and Advanced Technology for Digital Libraries
Robust Retrieval of Noisy Text
ADL '96 Proceedings of the 3rd International Forum on Research and Technology Advances in Digital Libraries
Classification of Object Sequences Using Syntactical Structure
Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
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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 correcting 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.