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A robust table registration method for batch table OCR processing

Published:24 August 2013Publication History

ABSTRACT

A robust table registration method is proposed in this paper for a better understanding on structured information from scanned table images. Scanned images can be heavily degraded because of scanning effects, binarization or purely document itself. For batch processing images with the same table structure, normally the table model is provided and can be used to overcome most challenging quality factors. The given table model is used as the ground truth in this paper. However, only rough precision is needed on table cell dimensions and this makes providing the table model an easier task. The method was tested on Multilingual Automatic Document Classification Analysis and Translation (MADCAT) images and a promising performance is achieved.

References

  1. Agency, D. A. R. P. Multilingual automatic document classification, analysis and translation (MADCAT). http://www.darpa.mil/Our_Work/I20/Programs/Multilingual_Automatic_Document_Classification,_Analysis_and_Translation_(MADCAT).aspx.Google ScholarGoogle Scholar
  2. Embley, D. W., Hurst, M., Lopresti, D., and Nagy, G. Table-processing paradigms: a research survey. International Journal of Document Analysis and Recognition 8, 2 (2006), 66--86.Google ScholarGoogle ScholarCross RefCross Ref
  3. Subramanian, K., Cao, H., Peng, X., Prasad, R., and Natarajan, P. Image registration and text recognition for structured census documents. In 12th Annual Workshop on Family History Technology (February 2012).Google ScholarGoogle Scholar
  4. Zanibbi, R., Blostein, D., and Cordy, J. R. A survey of table recognition. International Journal of Document Analysis and Recognition 7, 1 (2004), 1--16. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  • Published in

    cover image ACM Other conferences
    MOCR '13: Proceedings of the 4th International Workshop on Multilingual OCR
    August 2013
    99 pages
    ISBN:9781450321143
    DOI:10.1145/2505377

    Copyright © 2013 ACM

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 24 August 2013

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    • research-article

    Acceptance Rates

    MOCR '13 Paper Acceptance Rate17of34submissions,50%Overall Acceptance Rate17of34submissions,50%

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