Statistical binarization techniques for document image analysis

Research output: Contribution to journalReview article

2 Citations (Scopus)

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, clusteringbased, 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.

Original languageEnglish
Pages (from-to)23-36
Number of pages14
JournalJournal of Computer Science
Volume14
Issue number1
DOIs
Publication statusPublished - 3 Jan 2018

Fingerprint

Image analysis
Entropy
Binary images
Image enhancement
Color

Keywords

  • Binarization
  • Document image
  • Statistical features
  • Thresholding

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Artificial Intelligence

Cite this

Statistical binarization techniques for document image analysis. / Ismail, Saad M.; Sheikh Abdullah, Siti Norul Huda; Paizi@Fauzi, Wan Fariza.

In: Journal of Computer Science, Vol. 14, No. 1, 03.01.2018, p. 23-36.

Research output: Contribution to journalReview article

@article{58858ec1235f48d2b4e9073182a4ba11,
title = "Statistical binarization techniques for document image analysis",
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, clusteringbased, 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.",
keywords = "Binarization, Document image, Statistical features, Thresholding",
author = "Ismail, {Saad M.} and {Sheikh Abdullah}, {Siti Norul Huda} and Paizi@Fauzi, {Wan Fariza}",
year = "2018",
month = "1",
day = "3",
doi = "10.3844/jcssp.2018.23.36",
language = "English",
volume = "14",
pages = "23--36",
journal = "Journal of Computer Science",
issn = "1549-3636",
publisher = "Science Publications",
number = "1",

}

TY - JOUR

T1 - Statistical binarization techniques for document image analysis

AU - Ismail, Saad M.

AU - Sheikh Abdullah, Siti Norul Huda

AU - Paizi@Fauzi, Wan Fariza

PY - 2018/1/3

Y1 - 2018/1/3

N2 - 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, clusteringbased, 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.

AB - 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, clusteringbased, 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.

KW - Binarization

KW - Document image

KW - Statistical features

KW - Thresholding

UR - http://www.scopus.com/inward/record.url?scp=85041621868&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85041621868&partnerID=8YFLogxK

U2 - 10.3844/jcssp.2018.23.36

DO - 10.3844/jcssp.2018.23.36

M3 - Review article

AN - SCOPUS:85041621868

VL - 14

SP - 23

EP - 36

JO - Journal of Computer Science

JF - Journal of Computer Science

SN - 1549-3636

IS - 1

ER -