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Nonlinear diffusion equation with a dynamic threshold-based source for text binarization

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  • Du, Zhongjie
  • He, Chuanjiang

Abstract

Binarization for degraded text images has always been a very challenging issue due to the variety and complexity of degradations. In this paper, we first construct a thresholding function for the input image in a local manner and then present an anisotropic diffusion equation with a source involving dynamic thresholding function. This dynamic thresholding function is governed by an auxiliary evolution equation, taking the constructed thresholding function as the initial condition. In the diffusion equation, the diffusion term achieves the edge preserving smoothing, while the source term is response for designating dynamically the text and background pixels as two dominant modes separated by the final dynamic thresholding function. To evaluate the proposed model solely, we only utilize the simplest finite differencing rather than more elaborated scheme to solve it numerically. Experiments show that the proposed model has generally achieved the superior binarization results to other nine compared models.

Suggested Citation

  • Du, Zhongjie & He, Chuanjiang, 2024. "Nonlinear diffusion equation with a dynamic threshold-based source for text binarization," Applied Mathematics and Computation, Elsevier, vol. 482(C).
    • Handle: RePEc:eee:apmaco:v:482:y:2024:i:c:s0096300324004144
    DOI: 10.1016/j.amc.2024.128953
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    References listed on IDEAS

    as
    1. Wang, Yan & Zhou, Lingxin & Zhang, Xuyuan, 2023. "Spatio-temporal regularized shock-diffusion filtering with local entropy for restoration of degraded document images," Applied Mathematics and Computation, Elsevier, vol. 439(C).
    2. 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).
    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.
    5. Feng, Shu, 2022. "Effective document image binarization via a convex variational level set model," Applied Mathematics and Computation, Elsevier, vol. 419(C).
    6. 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).
    Full references (including those not matched with items on IDEAS)

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    1. 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).
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