1 October 1996 Comparative study of skew detection algorithms
Author Affiliations +
Abstract
Document image processing has become an increasingly important technology in the automation of office documentation tasks. Automatic document scanners such as text readers and optical character recognition (OCR) systems are an essential component of systems capable of those tasks. One of the problems in this field is that the document to be read is not always placed correctly 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 effect 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 realizing a practical document reader. We describe a new algorithm for skew detection. We then compare the performance and results of this skew detection algorithm to other published methods from O’Gorman, Hinds, Le, Baird, Postl, and Akiyama. Finally, we discuss the theory of skew detection and the different approaches taken to solve the problem of skew in documents. The skew correction algorithm we propose has been shown to be extremely fast, with run times averaging under 0.25 CPU seconds to calculate the angle on a DEC 5000/20 workstation.
Adnan Amin, Adnan Amin, Stephen Fischer, Stephen Fischer, Anthony F. Parkinson, Anthony F. Parkinson, Ricky Shiu, Ricky Shiu, } "Comparative study of skew detection algorithms," Journal of Electronic Imaging 5(4), (1 October 1996). https://doi.org/10.1117/12.245770 . Submission:
JOURNAL ARTICLE
9 PAGES


SHARE
RELATED CONTENT

Locally adaptive document skew detection
Proceedings of SPIE (April 02 1997)
Text segmentation for automatic document processing
Proceedings of SPIE (January 06 1999)
Segmenting Intersecting And Incomplete Boundaries
Proceedings of SPIE (March 28 1988)
Fast algorithm for skew detection
Proceedings of SPIE (March 04 1996)
Line detection algorithm based on random sample theory
Proceedings of SPIE (July 30 2002)

Back to Top