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Signature Detection and Matching for Document Image Retrieval
November 2009 (vol. 31 no. 11)
pp. 2015-2031
ASCII Text
x 
 
Guangyu Zhu, Yefeng Zheng, David Doermann, Stefan Jaeger, "Signature Detection and Matching for Document Image Retrieval," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 11, pp. 2015-2031, November, 2009.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2008.237,
author = {Guangyu Zhu and Yefeng Zheng and David Doermann and Stefan Jaeger},
title = {Signature Detection and Matching for Document Image Retrieval},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {31},
number = {11},
issn = {0162-8828},
year = {2009},
pages = {2015-2031},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2008.237},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Signature Detection and Matching for Document Image Retrieval
IS - 11
SN - 0162-8828
SP2015
EP2031
EPD - 2015-2031
A1 - Guangyu Zhu,
A1 - Yefeng Zheng,
A1 - David Doermann,
A1 - Stefan Jaeger,
PY - 2009
KW - Document image analysis and retrieval
KW - signature detection and segmentation
KW - signature matching
KW - structural saliency
KW - deformable shape
KW - measure of shape dissimilarity.
VL - 31
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Guangyu Zhu, University of Maryland, College Park
Yefeng Zheng, Siemens Corporate Research, Princeton
David Doermann, University of Maryland, College Park
Stefan Jaeger, CAS-MPG Partner Institute for Computational Biology, Shanghai
As one of the most pervasive methods of individual identification and document authentication, signatures present convincing evidence and provide an important form of indexing for effective document image processing and retrieval in a broad range of applications. However, detection and segmentation of free-form objects such as signatures from clustered background is currently an open document analysis problem. In this paper, we focus on two fundamental problems in signature-based document image retrieval. First, we propose a novel multiscale approach to jointly detecting and segmenting signatures from document images. Rather than focusing on local features that typically have large variations, our approach captures the structural saliency using a signature production model and computes the dynamic curvature of 2D contour fragments over multiple scales. This detection framework is general and computationally tractable. Second, we treat the problem of signature retrieval in the unconstrained setting of translation, scale, and rotation invariant nonrigid shape matching. We propose two novel measures of shape dissimilarity based on anisotropic scaling and registration residual error and present a supervised learning framework for combining complementary shape information from different dissimilarity metrics using LDA. We quantitatively study state-of-the-art shape representations, shape matching algorithms, measures of dissimilarity, and the use of multiple instances as query in document image retrieval. We further demonstrate our matching techniques in offline signature verification. Extensive experiments using large real-world collections of English and Arabic machine-printed and handwritten documents demonstrate the excellent performance of our approaches.

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Index Terms:
Document image analysis and retrieval, signature detection and segmentation, signature matching, structural saliency, deformable shape, measure of shape dissimilarity.
Citation:
Guangyu Zhu, Yefeng Zheng, David Doermann, Stefan Jaeger, "Signature Detection and Matching for Document Image Retrieval," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 11, pp. 2015-2031, Nov. 2009, doi:10.1109/TPAMI.2008.237
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