Document Skew Estimation by Instance-Based Learning
| Language | Japanese |
|---|---|
| Type | Letters |
| Authors | Seiichi Uchida, Megumi Sakai, Masakazu Iwamura, Shinichiro Omachi, Koichi Kise |
| Title | Document Skew Estimation by Instance-Based Learning |
| Journal | Trans. IEICE |
| Vol. | J91-D |
| No. | 1 |
| Pages | pp.136-138 |
| Reviewed | Reviewed |
| Date | January 2008 |
| Research fields | Pattern recognition |
| URL | http://search.ieice.org/bin/summary.php?id=j91-d_1_136 |
| Key | Uchida2008_IEICE (id=1000) |
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.
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