Japanese / English

Detail of Publication

Text Language Japanese
Authors Seiichi Uchida, Megumi Sakai, Masakazu Iwamura, Shinichiro Omachi, and Koichi Kise
Title Document Skew Estimation by Instance-Based Learning
Journal Trans. IEICE
Vol. J91-D
No. 1
Pages pp.136-138
Reviewed or not Reviewed
Month & Year January 2008
Abstract A novel document skew (i.e., rotation) estimation technique proposed in this paper has two properties: First, it estimates the rotation angle at each connected component. The entire rotation angle is determined by voting all the estimated rotation angles. Second, it employs instance-based learning where the rotation angle is estimated by referring stored instances each of which consists of a rotation invariant and a rotation variant. The result of a skew estimation experiment on 55 document images has shown that the skew angles of 54 document images were successfully estimated with errors smaller than 2.0 degrees.
URL http://search.ieice.org/bin/summary.php?id=j91-d_1_136&category=D&year=2008&lang=J&abst=
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