Harnessing Neural Networks for Enhancing Image Binarization Through Threshold Combination

Abstract

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 to image binarization, where the capacity of neural networks is utilized not just for determining optimal thresholds, but also for combining multiple global thresholds sourced from existing binarization techniques. The primary objective of our method is to develop a robust binarization strategy capable of managing a wide array of image conditions. By integrating the strengths of various thresholding techniques, our approach aims to establish a significant connection between traditional thresholding methods and those underpinned by deep learning.