Novel Adaptive Binarization Method for Degraded Document Images

Sheikh Abdullah, Siti Norul Huda and M. Ismail, Saad and Hasan, Mohammad Kamrul and Shivakumara, Palaiahnakote (2021) Novel Adaptive Binarization Method for Degraded Document Images. Computers, Materials & Continua, 67 (3). pp. 3815-3832. ISSN 1546-2226, DOI

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Achieving a good recognition rate for degraded document images is difficult as degraded document images suffer from low contrast, bleed-through, and nonuniform illumination effects. Unlike the existing baseline thresholding techniques that use fixed thresholds and windows, the proposed method introduces a concept for obtaining dynamic windows according to the image content to achieve better binarization. To enhance a low-contrast image, we proposed a new mean histogram stretching method for suppressing noisy pixels in the background and, simultaneously, increasing pixel contrast at edges or near edges, which results in an enhanced image. For the enhanced image, we propose a new method for deriving adaptive local thresholds for dynamic windows. The dynamic window is derived by exploiting the advantage of Otsu thresholding. To assess the performance of the proposed method, we have used standard databases, namely, document image binarization contest (DIBCO), for experimentation. The comparative study on well-known existing methods indicates that the proposed method outperforms the existing methods in terms of quality and recognition rate. © 2021 Tech Science Press. All rights reserved.

Item Type: Article
Funders: Ministry of Higher Education, Malaysia for providing facilities and financial support under the Long Research Grant Scheme LRGS-1-2019-UKM-UKM-2-7
Uncontrolled Keywords: Global and local thresholding; adaptive binarization; degraded document image; image histogram; document image binarization contest
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 25 May 2021 04:44
Last Modified: 25 May 2021 04:44

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