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Direct Unsupervised Text Line Extraction from Colored Historical Manuscript Images Using DCT

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Image Analysis and Recognition (ICIAR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9730))

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Abstract

Extracting lines of text from a manuscript is an important preprocessing step in many digital paleography applications. These extracted lines play a fundamental part in the identification of the author and/or age of the manuscript. In this paper we present an unsupervised approach to text line extraction in historical manuscripts that can be applied directly to a color manuscript image. Each of the red, green and blue channels are processed separately by applying DCT on them individually. One of the key advantages of this approach is that it can be applied directly to the manuscript image without any preprocessing, training or tuning steps. Extensive testing on complex Arabic handwritten manuscripts shows the effectiveness of the proposed approach.

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Acknowledgement

This publication was made possible by NPRP grant # NPRP NPRP7-442-1-082 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.

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Authors and Affiliations

  1. Qatar University, Doha, Qatar

    Asim Baig & Somaya Al-Maadeed

  2. Northumbria University, Newcastle upon Tyne, UK

    Ahmed Bouridane

  3. École de technologie supérieure, Montreal, Canada

    Mohamed Cheriet

Authors
  1. Asim Baig
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  2. Somaya Al-Maadeed
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  3. Ahmed Bouridane
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  4. Mohamed Cheriet
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Corresponding author

Correspondence to Asim Baig .

Editor information

Editors and Affiliations

  1. University of Porto, Porto, Portugal

    Aurélio Campilho

  2. Department of Electrical, University of Waterloo, Waterloo, Ontario, Canada

    Fakhri Karray

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Baig, A., Al-Maadeed, S., Bouridane, A., Cheriet, M. (2016). Direct Unsupervised Text Line Extraction from Colored Historical Manuscript Images Using DCT. In: Campilho, A., Karray, F. (eds) Image Analysis and Recognition. ICIAR 2016. Lecture Notes in Computer Science(), vol 9730. Springer, Cham. https://doi.org/10.1007/978-3-319-41501-7_84

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  • DOI: https://doi.org/10.1007/978-3-319-41501-7_84

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41500-0

  • Online ISBN: 978-3-319-41501-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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