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A locally adaptive, diffusion based text binarization technique

Author

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  • Jacobs, B.A.
  • Momoniat, E.

Abstract

This research proposes an adaptive modification to a novel diffusion based text binarization technique. This technique uses linear diffusion with a nonlinear source term to achieve a binarizing effect. This simple isotropic process is compared to the state-of-the-art DIBCO contestants and produces remarkable results given the simplicity of the algorithm. Furthermore, the authors show how using a simple discretization scheme allows for the massively parallel implementation of this process.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:apmaco:v:269:y:2015:i:c:p:464-472
    DOI: 10.1016/j.amc.2015.07.091
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    Cited by:

    1. 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).
    2. 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).
    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. Feng, Shu, 2022. "Effective document image binarization via a convex variational level set model," Applied Mathematics and Computation, Elsevier, vol. 419(C).
    5. 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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