Skew detection in document images based on rectangular active contour |
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Authors: | Huijie Fan Linlin Zhu Yandong Tang |
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Affiliation: | (1) Department of Physics and Computer Science, Dayalbagh Educational Institute, 282005 Agra, India;(2) Department of Electrical Engineering, Dayalbagh Educational Institute, 282005 Agra, India |
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Abstract: | The digitalization processes of documents produce frequently images with small rotation angles. The skew angles in document
images degrade the performance of optical character recognition (OCR) tools. Therefore, skew detection of document images
plays an important role in automatic document analysis systems. In this paper, we propose a Rectangular Active Contour Model
(RAC Model) for content region detection and skew angle calculation by imposing a rectangular shape constraint on the zero-level
set in Chan–Vese Model (C-V Model) according to the rectangular feature of content regions in document images. Our algorithm
differs from other skew detection methods in that it does not rely on local image features. Instead, it uses global image
features and shape constraint to obtain a strong robustness in detecting skew angles of document images. We experimented on
different types of document images. Comparing the results with other skew detection algorithms, our algorithm is more accurate
in detecting the skews of the complex document images with different fonts, tables, illustrations, and layouts. We do not
need to pre-process the original image, even if it is noisy, and at the same time the rectangular content region of a document
image is also detected. |
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Keywords: | |
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