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Improvement of Binarization Method Using a Water Flow Model for Document Images with Complex Backgrounds

  • Conference paper
PRICAI 2004: Trends in Artificial Intelligence (PRICAI 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3157))

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Abstract

Binarization algorithm using a water flow model has been presented [6], in which a document image is efficiently separated into two regions, characters and backgrounds, due to the property of locally adaptive thresholding. However, this method has not decided when to stop the iterative process and required long processing time. Moreover, characters on poor contrast backgrounds often fail to be separated successfully. In the current paper, an improved approach is proposed to overcome above shortcomings of the existing method, by introducing a hierarchical thresholding technique as well as extracting the regions of interest (ROIs) for speed-up and an automatic stopping criterion.

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References

  1. Cheng, H., Fan, Z.: Background Identification Based Segmentation and Multilayer Three Representation of Document Images. In: Proceedings of International Conference on Image Processing, vol. 3, pp. 1005–1008 (2002)

    Google Scholar 

  2. Sauvola, J., Pietikainen, M.: Adaptive Document Image Binarization. Pattern Recognition 33, 225–236 (2000)

    Article  Google Scholar 

  3. Otsu, N.: A Threshold Selection Method from Gray-Scale Histogram. IEEE Trans. Syst., Man, Cybern. SMC 8, 62–66 (1978)

    Article  Google Scholar 

  4. Kitter, J., Illingworth, J.: On Threshold Selection Using Clustering Criteria. IEEE Trans. Syst., Man, Cybern. SMC 15, 652–655 (1985)

    Google Scholar 

  5. O’Gorman, L.: Binarization and Multithresholding of Document Image Using Connectivity. Graphical Models Image Process 56(6), 494–506 (1994)

    Article  MathSciNet  Google Scholar 

  6. Kim, I.-K., Jung, D.-W., Park, R.-H.: Document Image Binarization Based on Topographic Analysis Using aWater Flow Model. Pattern Recognition 35, 265–277 (2002)

    Article  MATH  Google Scholar 

  7. Liu, Y., Srihari, S.N.: Document Image Binarization Based on Texture Features. IEEE Trans. Pattern Anal. Mach. Intell. 19(5), 540–544 (1997)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, Kyungpook National University, Daegu, 702-701, Korea

    Hyun-Hwa Oh & Sung-Il Chien

Authors
  1. Hyun-Hwa Oh
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  2. Sung-Il Chien
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Editor information

Editors and Affiliations

  1. Faculty of Engineering and Information Technology, Centre for Quantum Computation and Intelligent Systems, and Australian ACS National Committee for Artificial Intelligence, University of Technology, Sydney, Australia

    Chengqi Zhang

  2. Department of Computer Science, Auckland University of Technology, 1020, Auckland, New Zealand

    Hans W. Guesgen

  3. Artificial Intelligence Technology Centre, Auckland University of Technology, Auckland, New Zealand

    Wai-Kiang Yeap

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© 2004 Springer-Verlag Berlin Heidelberg

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Oh, HH., Chien, SI. (2004). Improvement of Binarization Method Using a Water Flow Model for Document Images with Complex Backgrounds. In: Zhang, C., W. Guesgen, H., Yeap, WK. (eds) PRICAI 2004: Trends in Artificial Intelligence. PRICAI 2004. Lecture Notes in Computer Science(), vol 3157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28633-2_86

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  • DOI: https://doi.org/10.1007/978-3-540-28633-2_86

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22817-2

  • Online ISBN: 978-3-540-28633-2

  • eBook Packages: Springer Book Archive

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