Gated Local Adaptive Binarization using Supervised Learning Javier Fumanal-Idocin 1 , Juan Uriarte 1 , Borja de la Osa 1 , Francesco Bardozzo 2 , Javier Fernández 1 and Humberto Bustince 1 1 Estadística, Informática, Matemáticas, Public University of Navarre 2 Neuronelab, DISA-MIS, University DegliStudy di Salerno Abstract 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 thresh- old for all the image. A solution for this problem is the adaptive thresholding, in which an image can have diferent thresholds for diferent 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 efectively learn the most appropri- ate threshold in each situation, and beats other adaptive binarization solutions for a standard dataset in the literature. Keywords Fuzzy logic, Image Thresholding, Image Processing, Aggregation functions 1. Introduction Image processing ins one of the most important research topics in the computer science areas [1, 2, 3]. Many problems have been studied in this area, like classifcation [4, 5, 6] and segmentation of diferent objects in an image [7]. One of the most researched topics in image processing is image thresholding [8, 9], also called image binarization, which consists of discriminating the objects in an image from the background. The most popular binarization algorithm is the Otsu algorithm [10], and many other popular algorithms have been proposed [11, 12, 13]. All of these algorithms work by establishing a global threshold for the whole image. However, this strategy results in poor performance when there are changes in the lightning and contrast of the image. In that case, the same threshold cannot adapt itself to the diferent conditions in the image. Adaptive thresholding was proposed in [14] as a mean to solve this problem, by choosing a diferent threshold for the diferent parts of the image. This algorithm works by precomputing WILF 2021: International Workshop on Fuzzy Logic and Applications  javier.fumanal@unavarra.es (J. Fumanal-Idocin); juan@losuriarte.es (J. Uriarte); borjajose.delaosa@unavarra.es (B. d. l. Osa); fbardozzo@unisa.it (F. Bardozzo); fcojavier.fernandez@unavara.es (J. Fernández); bustince@unavara.es (H. Bustince)  https://fuminides.netlify.app/ (J. Fumanal-Idocin)  0000-0002-0644-1355 (J. Fumanal-Idocin); 0000-0003-0199-6623 (F. Bardozzo) © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings http://ceur-ws.org ISSN1613-0073 CEUR Workshop Proceedings (CEUR-WS.org)