Robust detection of skew in document images

Abstract

We describe a robust yet fast algorithm for skew detection in binary document images. The method is based on interline cross-correlation in the scanned image. Instead of finding the correlation for the entire image, it is calculated over small regions selected randomly. The proposed method does not require prior segmentation of the document into text and graphics regions. The maximum median of cross-correlation is used as the criterion to obtain the skew, and a Monte Carlo sampling technique is chosen to determine the number of regions over which the correlations have to be calculated. Experimental results on detecting skews in various types of documents containing different linguistic scripts are presented here.