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Multi-scale Structural Saliency for Signature Detection

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Multi-scale Structural Saliency for Signature Detection
Detecting and segmenting free-form objects from cluttered backgrounds is a challenging problem in computer vision. Signature detection in document images is one classic example and as of yet no reasonable solutions have been presented. In this paper, we propose a novel multi-scale approach to jointly detecting and segmenting signatures from documents with diverse layouts and complex backgrounds. Rather than focusing on local features that typically have large variations, our approach aims to capture the structural saliency of a signature by searching over multiple scales. This detection framework is general and computationally tractable. We present a saliency measure based on a signature production model that effectively quantifies the dynamic curvature of 2-D contour fragments. Our evaluation using large real world collections of handwritten and machine printed documents demonstrates the effectiveness of this joint detection and segmentation approach.
Guangyu Zhu, Yefeng Zheng, David S. Doermann, Stef
Added 12 Oct 2009
Updated 28 Oct 2009
Type Conference
Year 2007
Where CVPR
Authors Guangyu Zhu, Yefeng Zheng, David S. Doermann, Stefan Jaeger
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