Review Article Open Access

Statistical Binarization Techniques for Document Image Analysis

Saad M. Ismail1, Siti Norul Huda Sheikh Abdullah1 and Fariza Fauzi1
  • 1 Universiti Kebangsaan Malaysia, Malaysia

Abstract

Binarization is an important process in image enhancement and analysis. Currently, numerous binarization techniques have been reported in the literature. These binarization methods produce binary images from color or gray-level images. This article highlights an extensive review on various binarization approaches which are also referred to as thresholding methods. These methods are grouped into seven categories according to the employed features and techniques: histogram shape-based, clustering-based, entropy-based, object-attribute-based, spatial, local and hybrid methods. Most active binarization researchers exploit several initial information from the source image such as histogram shape, measurement space clustering, entropy, object attributes, spatial correlation and local gray level surface with a special attention to statistical information description features of image used in recent thresholding techniques.

Journal of Computer Science
Volume 14 No. 1, 2018, 23-36

DOI: https://doi.org/10.3844/jcssp.2018.23.36

Submitted On: 3 September 2017 Published On: 3 January 2018

How to Cite: Ismail, S. M., Sheikh Abdullah, S. N. H. & Fauzi, F. (2018). Statistical Binarization Techniques for Document Image Analysis. Journal of Computer Science, 14(1), 23-36. https://doi.org/10.3844/jcssp.2018.23.36

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Keywords

  • Document Image
  • Statistical Features
  • Thresholding
  • Binarization