Combining one-and two-dimensional signal recognition approaches to off-line signature verification
Identifieur interne : 001175 ( Main/Exploration ); précédent : 001174; suivant : 001176Combining one-and two-dimensional signal recognition approaches to off-line signature verification
Auteurs : SIYUAN CHEN [États-Unis] ; Sargur Srihari [États-Unis]Source :
- Proceedings of SPIE, the International Society for Optical Engineering [ 0277-786X ] ; 2006.
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- Pascal (Inist)
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Abstract
A signature verification method that combines recognition methods of one-dimensional signals, e.g., speech and on-line handwriting, and two-dimensional images, e.g., holistic word recognition in OCR and off-line handwriting is described. In the one-dimensional approach, a sequence of data is obtained by tracing the exterior contour of the signature which allows the application of string-matching algorithms. The upper and lower contours of the signature are first determined by ignoring small gaps between signature components. The contours are combined into a single sequence so as to define a pseudo-writing path. To match two signatures a non-linear normalization method, viz., dynamic time warping, is applied to segment them into curves. Shape descriptors based on Zernike moments are extracted as features from each segment. A harmonic distance is used for measuring signature similarity. The two-dimensional approach is based on using features describing the word-shape. When the two methods are combined, the overall performance is significantly better than either method alone. With a database of 1320 genuines and 1320 forgeries the combination method has an accuracy of 90%.
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wicri:level="4"><inist:fA14 i1="01"><s1>Center of Excellence for Document Analysis and Recognition (CEDAR), University at Buffalo, State University of New York</s1><s2>Buffalo, 14228</s2><s3>USA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>États-Unis</country><wicri:noRegion>Buffalo, 14228</wicri:noRegion><orgName type="university">Université d'État de New York à Buffalo</orgName><placeName><settlement type="city">Buffalo (New York)</settlement><region type="state">État de New York</region></placeName></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">INIST</idno><idno type="inist">07-0377965</idno><date when="2006">2006</date><idno type="stanalyst">PASCAL 07-0377965 INIST</idno><idno type="RBID">Pascal:07-0377965</idno><idno type="wicri:Area/PascalFrancis/Corpus">000335</idno><idno type="wicri:Area/PascalFrancis/Curation">000451</idno><idno type="wicri:Area/PascalFrancis/Checkpoint">000345</idno><idno type="wicri:doubleKey">0277-786X:2006:Siyuan Chen:combining:one:and</idno><idno type="wicri:Area/Main/Merge">001206</idno><idno type="wicri:Area/Main/Curation">001175</idno><idno type="wicri:Area/Main/Exploration">001175</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a">Combining one-and two-dimensional signal recognition approaches to off-line signature verification</title><author><name sortKey="Siyuan Chen" sort="Siyuan Chen" uniqKey="Siyuan Chen" last="Siyuan Chen">SIYUAN CHEN</name><affiliation wicri:level="4"><inist:fA14 i1="01"><s1>Center of Excellence for Document Analysis and Recognition (CEDAR), University at Buffalo, State University of New York</s1><s2>Buffalo, 14228</s2><s3>USA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>États-Unis</country><wicri:noRegion>Buffalo, 14228</wicri:noRegion><orgName type="university">Université d'État de New York à Buffalo</orgName><placeName><settlement type="city">Buffalo (New York)</settlement><region type="state">État de New York</region></placeName></affiliation></author><author><name sortKey="Srihari, Sargur" sort="Srihari, Sargur" uniqKey="Srihari S" first="Sargur" last="Srihari">Sargur Srihari</name><affiliation wicri:level="4"><inist:fA14 i1="01"><s1>Center of Excellence for Document Analysis and Recognition (CEDAR), University at Buffalo, State University of New York</s1><s2>Buffalo, 14228</s2><s3>USA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>États-Unis</country><wicri:noRegion>Buffalo, 14228</wicri:noRegion><orgName type="university">Université d'État de New York à Buffalo</orgName><placeName><settlement type="city">Buffalo (New York)</settlement><region type="state">État de New York</region></placeName></affiliation></author></analytic><series><title level="j" type="main">Proceedings of SPIE, the International Society for Optical Engineering</title><idno type="ISSN">0277-786X</idno><imprint><date when="2006">2006</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">Proceedings of SPIE, the International Society for Optical Engineering</title><idno type="ISSN">0277-786X</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithm</term><term>Authentication</term><term>Handwriting recognition</term><term>Handwritten character recognition</term><term>Manuscript character</term><term>Optical character recognition</term><term>Pattern recognition</term><term>Two dimensional model</term><term>Two dimensional signal</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Algorithme</term><term>Signal bidimensionnel</term><term>Authentification</term><term>Reconnaissance écriture</term><term>Caractère manuscrit</term><term>Modèle 2 dimensions</term><term>Reconnaissance caractère manuscrit</term><term>Reconnaissance optique caractère</term><term>Reconnaissance forme</term><term>4230S</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">A signature verification method that combines recognition methods of one-dimensional signals, e.g., speech and on-line handwriting, and two-dimensional images, e.g., holistic word recognition in OCR and off-line handwriting is described. In the one-dimensional approach, a sequence of data is obtained by tracing the exterior contour of the signature which allows the application of string-matching algorithms. The upper and lower contours of the signature are first determined by ignoring small gaps between signature components. The contours are combined into a single sequence so as to define a pseudo-writing path. To match two signatures a non-linear normalization method, viz., dynamic time warping, is applied to segment them into curves. Shape descriptors based on Zernike moments are extracted as features from each segment. A harmonic distance is used for measuring signature similarity. The two-dimensional approach is based on using features describing the word-shape. When the two methods are combined, the overall performance is significantly better than either method alone. With a database of 1320 genuines and 1320 forgeries the combination method has an accuracy of 90%.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>État de New York</li></region><settlement><li>Buffalo (New York)</li></settlement><orgName><li>Université d'État de New York à Buffalo</li></orgName></list><tree><country name="États-Unis"><region name="État de New York"><name sortKey="Siyuan Chen" sort="Siyuan Chen" uniqKey="Siyuan Chen" last="Siyuan Chen">SIYUAN CHEN</name></region><name sortKey="Srihari, Sargur" sort="Srihari, Sargur" uniqKey="Srihari S" first="Sargur" last="Srihari">Sargur Srihari</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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