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Anisotropic diffusion with fuzzy-based source for binarization of degraded document images

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  • Du, Zhongjie
  • He, Chuanjiang

Abstract

Document image binarization plays a vital role in the document image analysis system; however, it remains challenging due to various degradations. In this paper, we propose an anisotropic diffusion model involving fuzzy-based source for binarizing degraded document images, in which the diffusion term is response for edge-preserving smoothing and the source term is used to group intensity values of foreground and background pixels into two dominant modes separated by zero. Specifically, a fuzzy classification function (FCF) is first introduced for vaguely separating foreground from background, which is defined in a local neighborhood of each point rather than in the entire image domain. Then, the fuzzy-based source is constructed by FCF and a speed restrictor, involving no threshold. In numerical aspects, we develop a parallel-serial algorithm by combining finite differencing and parallel/serial splitting methods in the literature. This algorithm is tested on seven publicly available datasets (DIBCO 2009 to 2014 and 2016) and compared with six PDE-based models and two variational models in terms of degraded document binarization. Experimental results illustrate that our model is very effective for binarization of degrade document images, and is superior to the compared models subjectively and objectively.

Suggested Citation

  • Du, Zhongjie & He, Chuanjiang, 2023. "Anisotropic diffusion with fuzzy-based source for binarization of degraded document images," Applied Mathematics and Computation, Elsevier, vol. 441(C).
    • Handle: RePEc:eee:apmaco:v:441:y:2023:i:c:s0096300322007524
    DOI: 10.1016/j.amc.2022.127684
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    References listed on IDEAS

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    1. Feng, Shu, 2022. "Effective document image binarization via a convex variational level set model," Applied Mathematics and Computation, Elsevier, vol. 419(C).
    2. Jacobs, B.A. & Celik, T., 2022. "Unsupervised document image binarization using a system of nonlinear partial differential equations," Applied Mathematics and Computation, Elsevier, vol. 418(C).
    3. Guo, Jiebin & He, Chuanjiang & Zhang, Xiaoting, 2019. "Nonlinear edge-preserving diffusion with adaptive source for document images binarization," Applied Mathematics and Computation, Elsevier, vol. 351(C), pages 8-22.
    4. Jacobs, B.A. & Momoniat, E., 2015. "A locally adaptive, diffusion based text binarization technique," Applied Mathematics and Computation, Elsevier, vol. 269(C), pages 464-472.
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