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An Efficient Form Classification Method Using Partial Matching

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Book cover AI 2001: Advances in Artificial Intelligence (AI 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2256))

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Abstract

In this paper, we are proposing an efficient method of classifying form that is applicable in real life. Our method will identify a small number of local regions by their distinctive images with respect to their layout structure and then by using the DP (Dynamic Programming) matching to match only these local regions. The disparity score in each local region is defined and measured to select the matching regions. Genetic Algorithm will also be applied to select the best regions of matching from the viewpoint of a performance. Our approach of searching and matching only a small number of structurally distinctive local regions would reduce the processing time and yield a high rate of classification.

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References

  1. S. L. Taylor, R. Fritzson, J. A. Pastor, Extraction of data from preprinted forms, Proceedings of IAPR Workshop on Machine Vision and Applications, Vol. 5, pp. 211–222

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Author information

Authors and Affiliations

  1. Dept. of Computer Software Research Laboratory, ETRI, Korea

    Yungcheol Byun

  2. Dept. of Computer Science, Yonsei University, Korea

    Sungsoo Yoon & Yillbyung Lee

  3. Dept. of Computer Science, Sookmyung Women’s University, Korea

    Yeongwoo Choi

  4. Dept. of Electronic Engineering, Sogang University, Korea

    Gyeonghwan Kim

Authors
  1. Yungcheol Byun
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  2. Sungsoo Yoon
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  3. Yeongwoo Choi
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  4. Gyeonghwan Kim
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  5. Yillbyung Lee
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Editor information

Editors and Affiliations

  1. School of Computer and Information Science, University of South Australia, Mawson Lakes, 5095, SA, Australia

    Markus Stumptner  & Dan Corbett  & 

  2. Department of Computer Science, University of Adelaide, 5001, Adelaide, SA, Australia

    Mike Brooks

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© 2001 Springer-Verlag Berlin Heidelberg

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Byun, Y., Yoon, S., Choi, Y., Kim, G., Lee, Y. (2001). An Efficient Form Classification Method Using Partial Matching. In: Stumptner, M., Corbett, D., Brooks, M. (eds) AI 2001: Advances in Artificial Intelligence. AI 2001. Lecture Notes in Computer Science(), vol 2256. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45656-2_9

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  • DOI: https://doi.org/10.1007/3-540-45656-2_9

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42960-9

  • Online ISBN: 978-3-540-45656-8

  • eBook Packages: Springer Book Archive

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