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KFBin: Kalman Filter-Based Approach for Document Image Binarization

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Image Analysis and Recognition (ICIAR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11662))

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Abstract

In this paper, we propose a novel two-step approach, called KFBin, for the binarization of document images based on the Kalman filtering (KF) technique. In the first step, a state space model is developed as a new document image representation, and then the Kalman filter is applied to track the positions of the foreground and background information and generate two corresponding outputs, which allows the enhancement of the foreground content leading to better legibility of text. Standard thresholding algorithms were used in the second step to generate binary images from the enhanced foreground components. The performance of the proposed approach is validated on a well-known dataset and evaluated using common image binarization quality metrics. Outstanding improvement of the binarization performances of several state-of-the-art binarization methods has been achieved by using the proposed approach. Experimental results point that the poor binarization results of egraded document images can be greatly improved by enhancing their quality.

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Author information

Authors and Affiliations

  1. Synchromedia Lab, École de Technologie Supérieure (ETS), University of Quebec, Montreal, Canada

    Abderrahmane Rahiche & Mohamed Cheriet

Authors
  1. Abderrahmane Rahiche
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  2. Mohamed Cheriet
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Corresponding author

Correspondence to Abderrahmane Rahiche .

Editor information

Editors and Affiliations

  1. University of Waterloo, Waterloo, ON, Canada

    Fakhri Karray

  2. University of Porto, Porto, Portugal

    Aurélio Campilho

  3. University of Waterloo, Waterloo, ON, Canada

    Alfred Yu

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Rahiche, A., Cheriet, M. (2019). KFBin: Kalman Filter-Based Approach for Document Image Binarization. In: Karray, F., Campilho, A., Yu, A. (eds) Image Analysis and Recognition. ICIAR 2019. Lecture Notes in Computer Science(), vol 11662. Springer, Cham. https://doi.org/10.1007/978-3-030-27202-9_13

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  • DOI: https://doi.org/10.1007/978-3-030-27202-9_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27201-2

  • Online ISBN: 978-3-030-27202-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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