  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.
