Document image processing has become an increasingly important technology i
n the automation of office documentation tasks. Automatic document scanners
such as text readers and OCR (Optical Character Recognition) systems are a
n essential component of systems capable of those tasks. One of the problem
s in this held is that the document to br read is not always placed correct
ly on a flat-bed scanner. This means that the document may be skewed on the
scanner bed, resulting in a skewed image. This skew has a detrimental effe
ct on document analysis, document understanding, and character segmentation
and recognition, Consequently, detecting the skew of a document image and
correcting it are important issues in realising a practical document reader
. in this: paper we describe a new algorithm for skew detection. We then co
mpare the performance and results of this skew detection algorithm to other
published methods from O'Gorman, Hinds, Le, Baird, Postl and Akiyama. Fina
lly, we discuss the theory of skew detection and the different approaches t
aken cu solve the problem of skew in documents. The skew correction algorit
hm we purpose has been shown tu br extremely fast, with run times averaging
under 0.25 CPU seconds to calculate the angle on a DEC 5000/20 workstation
.