Fast and robust skew correction in scanned document images based on low-rank matrix decompositon

The most important of skew correction for scanned document image is to estimate the skew angle. Traditional methods mostly based on its linear check, such as Hough transformation and so on. However, it is often affected by its texture structure or other noise. In this paper, a fast and robust skew e...

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Bibliographic Details
Main Authors: Heng-You Wang, Rui-Zhen Zhao, Jing-An Cui
Format: Conference Proceeding
Language:English
Subjects:
Abstracts
Approximation methods
Hough Transformation
Lead
Low-Rank Matrix Decomposition
Matching pursuit algorithms
Matrix decomposition
Robustness
Scanned Document Images
Skew Correction
Transforms
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Summary:The most important of skew correction for scanned document image is to estimate the skew angle. Traditional methods mostly based on its linear check, such as Hough transformation and so on. However, it is often affected by its texture structure or other noise. In this paper, a fast and robust skew estimation method is proposed based on low-rank matrix decomposition, which seeks an affine transformation that can be used to implement the correction. As the rank of a matrix is a natural measure of regularity and symmetry of images, a misaligned scanned document image is assumed to be correct when the rank of the texture extracted from the image itself is the minimum. Therefore, the skew correction problem can be considered as a matrix rank minimization problem. As experiment illustrated, our method works efficiently and robustly overcoming corruptions, such as lines, circles and so on.
ISSN:2160-133X
DOI:10.1109/ICMLC.2014.7009726