Modified adaptive thresholding using integral image

Abstract

Adaptive thresholding, the simple way to perform image segmentation, is a form of image thresholding used to classify pixels as dark and light. Taking grayscale image as an input for this task is only good in case that text appears in low intensity area. In case that text appears in high intensity area, it leads to lower recall rate for text detection process. However, it can be fixed by taking an inverted-grayscale image instead. The problem is how to determine automatically whether it is better to take the normal-grayscale or inverted-grayscale image for each original image. This paper proposes the simple way to do that by means of the adaptive thresholding using the integral image itself with some additional steps based on its principle. The proposed method consists of two main process, low/high intensity area segmentation and modified adaptive thresholding.