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Character Recognition | 2 |
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Taghva, Kazem | 1 |
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Sun, Wei; And Others – Journal of the American Society for Information Science, 1992
Identifies types and distributions of errors in text produced by optical character recognition (OCR) and proposes a process using machine learning techniques to recognize and correct errors in OCR texts. Results of experiments indicating that this strategy can reduce human interaction required for error correction are reported. (25 references)…
Descriptors: Artificial Intelligence, Automation, Character Recognition, Error Correction

Taghva, Kazem; And Others – Information Processing & Management, 1996
Reports on the performance of the vector space model in the presence of OCR (optical character recognition) errors in information retrieval. Highlights include precision and recall, a full-text test collection, smart vector representation, impact of weighting parameters, ranking variability, and the effect of relevance feedback. (Author/LRW)
Descriptors: Analysis of Variance, Character Recognition, Feedback, Full Text Databases