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.

中文翻译：

---

具有基于动态阈值的文本二值化源的非线性扩散方程


由于退化的多样性和复杂性，退化文本图像的二值化一直是一个非常具有挑战性的问题。在本文中，我们首先以局部方式构造输入图像的阈值函数，然后提出具有涉及动态阈值函数的源的各向异性扩散方程。该动态阈值函数由辅助演化方程控制，以构造的阈值函数作为初始条件。在扩散方程中，扩散项实现了边缘保留平滑，而源项是将文本和背景像素动态指定为由最终动态阈值函数分隔的两种主要模式的响应。为了单独评估所提出的模型，我们仅使用最简单的有限差分而不是更复杂的方案来对其进行数值求解。实验表明，所提出的模型总体上取得了优于其他九个对比模型的二值化结果。