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Scanned Document Images Skew Correction Based on Shearlet Transform

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Multi-disciplinary Trends in Artificial Intelligence (MIWAI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9426))

Abstract

During the documents scanning process, a few degrees of skew is unavoidable. Which will cause some adverse effects on the documents identification, such as Optical Character Recognition (ORC). This paper presents a scanned document images skew estimation and correction algorithm based on Shearlet transform. Shearlet transform offers very good time-frequency localization and direction selectivity. It is possible to detect the skew orientation of the document images accurately. Experimental results show that the proposed algorithm has a high accuracy rate of skew estimation even the scanned document images contain noise or include some pictures or diagrams.

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Acknowledgments

This research was supported by the Foundation of Education Bureau of Henan Province, China grants No. 2010B520003, Key Science and Technology Program of Henan Province, China grants No. 132102210133 and 132102210034, and the Key Science and Technology Projects of Public Health Department of Henan Province, China grants No. 2011020114.

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Authors and Affiliations

  1. School of Computer and Information Engineering, Henan University, Kaifeng, 475001, China

    Fan Zhang, Yifan Zhang, Xingxing Qu, Bin Liu & Ruoya Zhang

  2. Institute of Image Processing and Pattern Recognition, Henan University, Kaifeng, 475001, China

    Fan Zhang

Authors
  1. Fan Zhang
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  2. Yifan Zhang
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  3. Xingxing Qu
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  4. Bin Liu
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  5. Ruoya Zhang
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Corresponding author

Correspondence to Fan Zhang .

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Editors and Affiliations

  1. University College of London, London, United Kingdom

    Antonis Bikakis

  2. Fuzhou University, Fuzhou, China

    Xianghan Zheng

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© 2015 Springer International Publishing Switzerland

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Zhang, F., Zhang, Y., Qu, X., Liu, B., Zhang, R. (2015). Scanned Document Images Skew Correction Based on Shearlet Transform. In: Bikakis, A., Zheng, X. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2015. Lecture Notes in Computer Science(), vol 9426. Springer, Cham. https://doi.org/10.1007/978-3-319-26181-2_21

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  • DOI: https://doi.org/10.1007/978-3-319-26181-2_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26180-5

  • Online ISBN: 978-3-319-26181-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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