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The effect of OCR errors on stylistic text classification
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Source Annual ACM Conference on Research and Development in Information Retrieval archive
Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval table of contents
Seattle, Washington, USA
POSTER SESSION: Posters table of contents
Pages: 701 - 702  
Year of Publication: 2006
ISBN:1-59593-369-7
Authors
Sterling Stuart Stein  Illinois Institute of Technology, Chicago, IL
Shlomo Argamon  Illinois Institute of Technology, Chicago, IL
Ophir Frieder  Illinois Institute of Technology, Chicago, IL
Sponsors
SIGIR: ACM Special Interest Group on Information Retrieval
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 13,   Downloads (12 Months): 56,   Citation Count: 1
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ABSTRACT

Recently, interest is growing in non-topical text classification tasks such as genre classification, sentiment analysis, and authorship profiling. We study to what extent OCR errors affect stylistic text classification from scanned documents. We find that even a relatively high level of errors in the OCRed documents does not substantially affect stylistic classification accuracy.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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V. Levenshtein. Binary codes capable of correcting spurious insertions and deletions of ones. Problems of Information Transmission, 1:8--17, 1965.
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B. K. O. Uzuner. A comparative study of language models for book and author recognition. Springer-Verlag GmbH, page 969, 2005.
 
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Collaborative Colleagues:
Sterling Stuart Stein: colleagues
Shlomo Argamon: colleagues
Ophir Frieder: colleagues