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Using Scale-Space Anisotropic Smoothing for Text Line Extraction in Historical Documents

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Image Analysis and Recognition (ICIAR 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8814))

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Abstract

Text line extraction is vital pre-requisite for various document processing tasks. This paper presents a novel approach for text line extraction which is based on Gaussian scale space and dedicated binarization that utilize the inherent structure of smoothed text document images. It enhances the text lines in the image using multi-scale anisotropic second derivative of Gaussian filter bank at the average height of the text line. It then applies a binarization, which is based on component-tree and is tailored towards line extraction. The final stage of the algorithm is based on an energy minimization framework for removing spurious text line and assigning connected components to lines. We have tested our approach on various datasets written in different languages at range of image quality and received high detection rates, which outperform state-of-the-art algorithms. Our MATLAB code is publicly available. (http://www.cs.bgu.ac.il/~rafico/LineExtraction.zip)

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Author information

Authors and Affiliations

  1. Department of Computer Science, Ben-Gurion University, Beer-Sheva, Israel

    Rafi Cohen, Jihad El-Sana & Klara Kedem

  2. Department of Electrical and Computer Engineering, Ben-Gurion University, Beer-Sheva, Israel

    Itshak Dinstein

Authors
  1. Rafi Cohen
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  2. Itshak Dinstein
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  3. Jihad El-Sana
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  4. Klara Kedem
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Corresponding author

Correspondence to Rafi Cohen .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Porto, Porto, Portugal

    Aurélio Campilho

  2. Dept. of Electrical and Computer Eng., University of Waterloo, Waterloo, Ontario, Canada

    Mohamed Kamel

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© 2014 Springer International Publishing Switzerland

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Cohen, R., Dinstein, I., El-Sana, J., Kedem, K. (2014). Using Scale-Space Anisotropic Smoothing for Text Line Extraction in Historical Documents. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2014. Lecture Notes in Computer Science(), vol 8814. Springer, Cham. https://doi.org/10.1007/978-3-319-11758-4_38

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  • DOI: https://doi.org/10.1007/978-3-319-11758-4_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11757-7

  • Online ISBN: 978-3-319-11758-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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