@article {10.3844/jcssp.2018.23.36,
article_type = {journal},
title = {Statistical Binarization Techniques for Document Image Analysis},
author = {Ismail, Saad M. and Sheikh Abdullah, Siti Norul Huda and Fauzi, Fariza},
volume = {14},
number = {1},
year = {2018},
month = {Jan},
pages = {23-36},
doi = {10.3844/jcssp.2018.23.36},
url = {https://thescipub.com/abstract/jcssp.2018.23.36},
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 = {Journal of Computer Science},
publisher = {Science Publications}
}