Harnessing Neural Networks for Enhancing Image Binarization Through Threshold Combination

Threshold-based methods are prevalent across numerous domains, with specific relevance to image binarization, which traditionally employs global and local threshold algorithms. This paper presents a novel approach where the capacity of neural networks is utilized not just for determining optimal thresholds, but also combining multiple thresholds sourced from existing binarization techniques. The primary objective our method develop robust strategy capable managing wide array conditions. By integrating strengths various thresholding techniques, aims establish significant connection between traditional those underpinned by deep learning.