LANGUAGE INDEPENDENT ROBUST SKEW DETECTION AND CORRECTION TECHNIQUE FOR DOCUMENT IMAGES

Abstract

Document image processing is an increasingly important technology essential in all optical character recognition (OCR) systems and for automation of various office documents. A document originally has zero-skew (tilt), but when a page is scanned or photo copied, skew may be introduced due to various factors and is practically unavoidable. Presence even a small amount of skew (0.50) will have detrimental effects on document analysis as it has a direct effect on the reliability and efficiency of segmentation, recognition and feature extraction stages. Therefore removal of skew is of paramount importance in the field of document analysis and OCR and is the first step to be accomplished. This paper presents a novel technique for skew detection and correction which is both language and content independent. The proposed technique is based on the maximum density of the foreground pixels and their orientation in the document image. Unlike other conventional algorithms which work only for machine printed textual documents scripted in English, this technique works well for all kinds of document images (machine printed, hand written, complex, noisy and simple). The technique presented here is tested with 150 different document image samples and is found to provide results with an accuracy of 0.10