Recovery of temporal information from static images of handwriting
Identifieur interne : 002A57 ( Main/Exploration ); précédent : 002A56; suivant : 002A58Recovery of temporal information from static images of handwriting
Auteurs : S. Doermann [États-Unis] ; Azriel Rosenfeld [États-Unis]Source :
- International Journal of Computer Vision [ 0920-5691 ] ; 1995-06-01.
Abstract
Abstract: The problem of off-line handwritten character recognition has eluded a satisfactory solution for several decades. Researchers working in the area of on-line recognition have had greater success, but the possibility of extracting on-line information from static images has not been fully explored. The experience of forensic document examiners assures us that in many cases, such information can be successfully recovered. We outline the design of a system for the recovery of temporal information from static handwritten images. We provide a taxonomy of local, regional and global temporal clues which are often found in hand-written samples, and describe methods for recovering these clues from the image. We show how this system can benefit from obtaining a comprehensive understanding of the handwriting signal and a detailed analysis of stroke and sub-stroke properties. We suggest that the recovery task requires that we break away from traditional thresholding and thinning techniques, and we provide a framework for such analysis. We demonstrate how isolated temporal clues can reliably be extracted from this framework and propose a control structure for integrating the partial information. We show how many seemingly ambiguous situations can be resolved by the derived clues and our knowledge of the writing process, and provide several examples to illustrate our approach.
Url:
DOI: 10.1007/BF01450853
Affiliations:
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Doermann</name><affiliation wicri:level="2"><country xml:lang="fr">États-Unis</country><placeName><region type="state">Maryland</region></placeName><wicri:cityArea>Document Processing Group, Center for Automation Research, University of Maryland, 20742-3411, College Park</wicri:cityArea></affiliation></author><author><name sortKey="Rosenfeld, Azriel" sort="Rosenfeld, Azriel" uniqKey="Rosenfeld A" first="Azriel" last="Rosenfeld">Azriel Rosenfeld</name><affiliation wicri:level="2"><country xml:lang="fr">États-Unis</country><placeName><region type="state">Maryland</region></placeName><wicri:cityArea>Document Processing Group, Center for Automation Research, University of Maryland, 20742-3411, College Park</wicri:cityArea></affiliation></author></analytic><monogr></monogr><series><title level="j">International Journal of Computer Vision</title><title level="j" type="abbrev">Int J Comput Vision</title><idno type="ISSN">0920-5691</idno><idno type="eISSN">1573-1405</idno><imprint><publisher>Kluwer Academic Publishers</publisher><pubPlace>Dordrecht</pubPlace><date type="published" when="1995-06-01">1995-06-01</date><biblScope unit="volume">15</biblScope><biblScope unit="issue">1-2</biblScope><biblScope unit="page" from="143">143</biblScope><biblScope unit="page" to="164">164</biblScope></imprint><idno type="ISSN">0920-5691</idno></series><idno type="istex">C1B04C00CC4642C16E5DBD5475B80960837B2BF4</idno><idno type="DOI">10.1007/BF01450853</idno><idno type="ArticleID">BF01450853</idno><idno type="ArticleID">Art7</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0920-5691</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: The problem of off-line handwritten character recognition has eluded a satisfactory solution for several decades. Researchers working in the area of on-line recognition have had greater success, but the possibility of extracting on-line information from static images has not been fully explored. The experience of forensic document examiners assures us that in many cases, such information can be successfully recovered. We outline the design of a system for the recovery of temporal information from static handwritten images. We provide a taxonomy of local, regional and global temporal clues which are often found in hand-written samples, and describe methods for recovering these clues from the image. We show how this system can benefit from obtaining a comprehensive understanding of the handwriting signal and a detailed analysis of stroke and sub-stroke properties. We suggest that the recovery task requires that we break away from traditional thresholding and thinning techniques, and we provide a framework for such analysis. We demonstrate how isolated temporal clues can reliably be extracted from this framework and propose a control structure for integrating the partial information. We show how many seemingly ambiguous situations can be resolved by the derived clues and our knowledge of the writing process, and provide several examples to illustrate our approach.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Maryland</li></region></list><tree><country name="États-Unis"><region name="Maryland"><name sortKey="Doermann, S" sort="Doermann, S" uniqKey="Doermann S" first="S." last="Doermann">S. Doermann</name></region><name sortKey="Rosenfeld, Azriel" sort="Rosenfeld, Azriel" uniqKey="Rosenfeld A" first="Azriel" last="Rosenfeld">Azriel Rosenfeld</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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