Gated Local Adaptive Binarization using Supervised Learning

Image thresholding is one of the most popular problems in image processing. However, changes in lightning and contrast in an image can cause trouble for the existing algorithms that use a global threshold for all the image. A solution for this problem is the adaptive thresholding, in which an image can have different thresholds for different parts of the image. Yet, the problem of choosing the most suitable threshold for each region of the image is still open. In this paper we present the Gated Local Adaptive Binarization algorithm, in which we choose the most appropriate threshold for each region of the image using a logistic regression. Our results show that this algorithm can effectively learn the most appropriate threshold in each situation, and beats other adaptive binarization solutions for a standard dataset in the literature.