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The effect of OCR errors on stylistic text classification

Published: 06 August 2006 Publication History

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

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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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D. D. Lewis, S. Argamon, G. Agam, O. Frieder, D. Grossman, and J. Heard. Building a test collection for complex document information processing. In SIGIR-06, 2006.
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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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I. Witten, E. Frank, L. Trigg, M. Hall, G. Holmes, and S. Cunningham. Weka: Practical machine learning tools and techniques with java implementations, 1999.

Cited By

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  • (2024)Comparative Study on Synthetic and Natural Error Analysis with BART & MarianMT2024 IEEE 9th International Conference for Convergence in Technology (I2CT)10.1109/I2CT61223.2024.10543923(1-6)Online publication date: 5-Apr-2024
  • (2022)Evaluating the Impact of OCR Quality on Short Texts Classification TaskAdvances in Computational Intelligence10.1007/978-3-031-19496-2_13(163-177)Online publication date: 23-Oct-2022
  • (2006)A complex document information processing prototypeProceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval10.1145/1148170.1148274(599-600)Online publication date: 6-Aug-2006

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cover image ACM Conferences
SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
August 2006
768 pages
ISBN:1595933697
DOI:10.1145/1148170
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 August 2006

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Author Tags

  1. OCR
  2. OCR errors
  3. text classification

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SIGIR06
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SIGIR06: The 29th Annual International SIGIR Conference
August 6 - 11, 2006
Washington, Seattle, USA

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

View all
  • (2024)Comparative Study on Synthetic and Natural Error Analysis with BART & MarianMT2024 IEEE 9th International Conference for Convergence in Technology (I2CT)10.1109/I2CT61223.2024.10543923(1-6)Online publication date: 5-Apr-2024
  • (2022)Evaluating the Impact of OCR Quality on Short Texts Classification TaskAdvances in Computational Intelligence10.1007/978-3-031-19496-2_13(163-177)Online publication date: 23-Oct-2022
  • (2006)A complex document information processing prototypeProceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval10.1145/1148170.1148274(599-600)Online publication date: 6-Aug-2006

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