A New Local Adaptive Thresholding Technique in Binarization

TL;DR

This paper introduces a fast local adaptive thresholding method for image binarization that uses integral images to efficiently compute local means without standard deviation calculations, improving speed especially on large windows.

Contribution

The proposed technique employs integral images for rapid local mean computation, eliminating the need for standard deviation calculations, thus enhancing processing speed in binarization.

Findings

Faster binarization process compared to traditional methods

Effective in degraded document images with noise and contrast variation

Reduces computational complexity by avoiding standard deviation calculations

Abstract

Image binarization is the process of separation of pixel values into two groups, white as background and black as foreground. Thresholding plays a major in binarization of images. Thresholding can be categorized into global thresholding and local thresholding. In images with uniform contrast distribution of background and foreground like document images, global thresholding is more appropriate. In degraded document images, where considerable background noise or variation in contrast and illumination exists, there exists many pixels that cannot be easily classified as foreground or background. In such cases, binarization with local thresholding is more appropriate. This paper describes a locally adaptive thresholding technique that removes background by using local mean and mean deviation. Normally the local mean computational time depends on the window size. Our technique uses integral sum image as a prior processing to calculate local mean. It does not involve calculations of standard deviations as in other local adaptive techniques. This along with the fact that calculations of mean is independent of window size speed up the process as compared to other local thresholding techniques.