Efficient implementation of local adaptive thresholding techniques using integral images

Adaptive binarization is an important first step in many document analysis and OCR processes. This paper describes a fast adaptive binarization algorithm that yields the same quality of binarization as the Sauvola method,¹ but runs in time close to that of global thresholding methods (like Otsu's method²), independent of the window size. The algorithm combines the statistical constraints of Sauvola's method with integral images.³ Testing on the UW-1 dataset demonstrates a 20-fold speedup compared to the original Sauvola algorithm.