USING AN ANT COLONY OPTIMIZATION ALGORITHM FOR IMAGE EDGE DETECTION AS A THRESHOLD SEGMENTATION FOR OCR SYSTEM

摘要

Binarization or thresholding is one problem that have to solve in pattern recognition methods and applications. Moreover, it has a very important influence on the sequent steps in computer vision applications such as, Optical Character Recognition (OCR), image segmentation, and tracking objects. Ant colony optimization (ACO) is a population-based metaheuristic which use to solve optimizations problems in diverse fields, such as traffic congestion and control, structural optimization, manufacturing, and genomics are presented. In this work, a combination of ant colony, edge detection, and thresholding methods are combine in order to use in OCR system. The algorithm which is used the DIBCO 2009 in printed and a handwritten image was tested. This method has a compare with Kittler and Illingworth's Minimum Error Thresholding, potential difference, max entropy, Pirahansiahs method and Otsu.