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Proceedings Paper

Locally adaptive document skew detection
Author(s): Jaakko J. Sauvola; David Scott Doermann; Matti Pietikaeinen
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Paper Abstract

This paper proposes a new approach to the detection of local orientation and skew in document images. It is based on the observation that there are many documents where a single global estimate of the page skew is not sufficient. These documents require local adaptation to deal robustly with todays complex configurations of components on the page. The approach attempts to identify regions in the image which exhibit locally consistent physical properties and consistent physical properties and consistent orientation. To do this, we rapidly compute a coarse segmentation and delineate regions which differ with respect to layout and/or physical content. Each region is classified as text, graphics, mixed text/graphics, image or background using local features and additional features are extracted to estimate orientation. The local orientation decisions are propagated where appropriate to resolve ambiguity and to produce a global estimate of the skew for the page. The implementation of our algorithms is demonstrated on a set of images which have multiple regions with different orientations.

Paper Details

Date Published: 3 April 1997
PDF: 13 pages
Proc. SPIE 3027, Document Recognition IV, (3 April 1997); doi: 10.1117/12.270063
Show Author Affiliations
Jaakko J. Sauvola, Univ. of Oulu (Finland)
David Scott Doermann, Univ. of Maryland/College Park (United States)
Matti Pietikaeinen, Univ. of Oulu (Finland)

Published in SPIE Proceedings Vol. 3027:
Document Recognition IV
Luc M. Vincent; Jonathan J. Hull, Editor(s)

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