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Document Image Binarization Technique for Degraded Document Images

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International Journal of Computer Applications
© 2015 by IJCA Journal
Volume 122 - Number 22
Year of Publication: 2015
Authors:
Supriya Lokhande
N. A. Dawande
10.5120/21858-5183

Supriya Lokhande and N.a.dawande. Article: Document Image Binarization Technique for Degraded Document Images. International Journal of Computer Applications 122(22):22-29, July 2015. Full text available. BibTeX

@article{key:article,
	author = {Supriya Lokhande and N.a.dawande},
	title = {Article: Document Image Binarization Technique for Degraded Document Images},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {122},
	number = {22},
	pages = {22-29},
	month = {July},
	note = {Full text available}
}

Abstract

Document image binarization is a vital pre-processing technique for document image analysis that segments text from badly degraded document images. In this paper, we propose a robust document image binarization technique that is based on the concept of adaptive image contrast. The adaptive image contrast which is formed by combining local image contrast and the local image gradient makes it tolerant to text and background variation caused by different types of document degradations. In the proposed technique the adaptive contrast map is binarized and text stroke edge pixels are detected using Canny's algorithm. The document text is further segmented by a local threshold that is assessed in light of the intensities of detected text stroke edge pixels within a local window. The above mentioned process has been rehashed by combining adaptive image contrast with Sobel's Edge detection technique and Total Variation Edge Detection technique respectively A comparison between these techniques is then made on the basis of Peak-signal to Noise Ratio and Mean Square Error values. These methods have been tested on images suffering from different types of degradations . It has been found out that adaptive image contrast used with Canny's edge detection technique gives the best results.

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