Feature extraction methods for character recognition-A survey
Identifieur interne : 002675 ( Main/Exploration ); précédent : 002674; suivant : 002676Feature extraction methods for character recognition-A survey
Auteurs : Ivind Due Trier [Norvège] ; Anil K. Jain [États-Unis] ; Torfinn Taxt [États-Unis]Source :
- Pattern Recognition [ 0031-3203 ] ; 1995.
Abstract
This paper presents an overview of feature extraction methods for off-line recognition of segmented (isolated) characters. Selection of a feature extraction method is probably the single most important factor in achieving high recognition performance in character recognition systems. Different feature extraction methods are designed for different representations of the characters, such as solid binary characters, character contours, skeletons (thinned characters) or gray-level subimages of each individual character. The feature extraction methods are discussed in terms of invariance properties, reconstructability and expected distortions and variability of the characters. The problem of choosing the appropriate feature extraction method for a given application is also discussed. When a few promising feature extraction methods have been identified, they need to be evaluated experimentally to find the best method for the given application.
Url:
DOI: 10.1016/0031-3203(95)00118-2
Affiliations:
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Box 1080 Blindern, N-0316 Oslo</wicri:regionArea><wicri:noRegion>N-0316 Oslo</wicri:noRegion></affiliation></author><author><name sortKey="Jain, Anil K" sort="Jain, Anil K" uniqKey="Jain A" first="Anil K." last="Jain">Anil K. Jain</name><affiliation wicri:level="4"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Computer Science, Michigan State University, A714 Wells Hall, East Lansing, MI 48824-1027</wicri:regionArea><placeName><region type="state">Michigan</region><settlement type="city">East Lansing</settlement></placeName><orgName type="university">Université d'État du Michigan</orgName></affiliation></author><author><name sortKey="Taxt, Torfinn" sort="Taxt, Torfinn" uniqKey="Taxt T" first="Torfinn" last="Taxt">Torfinn Taxt</name><affiliation wicri:level="2"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Informatics, University of Oslo, P.O. Box 1080 Blindern, N-0316 Oslo, NorwayDepartment of Computer Science, Michigan State University, A714 Wells Hall, East Lansing, MI 48824-1027</wicri:regionArea><placeName><region type="state">Michigan</region></placeName></affiliation></author></analytic><monogr></monogr><series><title level="j">Pattern Recognition</title><title level="j" type="abbrev">PR</title><idno type="ISSN">0031-3203</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="1995">1995</date><biblScope unit="volume">29</biblScope><biblScope unit="issue">4</biblScope><biblScope unit="page" from="641">641</biblScope><biblScope unit="page" to="662">662</biblScope></imprint><idno type="ISSN">0031-3203</idno></series><idno type="istex">86EDB1DF24D14B34BB828A2FAC5B07E4E7B31722</idno><idno type="DOI">10.1016/0031-3203(95)00118-2</idno><idno type="PII">0031-3203(95)00118-2</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0031-3203</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This paper presents an overview of feature extraction methods for off-line recognition of segmented (isolated) characters. Selection of a feature extraction method is probably the single most important factor in achieving high recognition performance in character recognition systems. Different feature extraction methods are designed for different representations of the characters, such as solid binary characters, character contours, skeletons (thinned characters) or gray-level subimages of each individual character. The feature extraction methods are discussed in terms of invariance properties, reconstructability and expected distortions and variability of the characters. The problem of choosing the appropriate feature extraction method for a given application is also discussed. When a few promising feature extraction methods have been identified, they need to be evaluated experimentally to find the best method for the given application.</div></front></TEI><affiliations><list><country><li>Norvège</li><li>États-Unis</li></country><region><li>Michigan</li></region><settlement><li>East Lansing</li></settlement><orgName><li>Université d'État du Michigan</li></orgName></list><tree><country name="Norvège"><noRegion><name sortKey="Due Trier, Ivind" sort="Due Trier, Ivind" uniqKey="Due Trier " first=" Ivind" last="Due Trier"> Ivind Due Trier</name></noRegion></country><country name="États-Unis"><region name="Michigan"><name sortKey="Jain, Anil K" sort="Jain, Anil K" uniqKey="Jain A" first="Anil K." last="Jain">Anil K. Jain</name></region><name sortKey="Taxt, Torfinn" sort="Taxt, Torfinn" uniqKey="Taxt T" first="Torfinn" last="Taxt">Torfinn Taxt</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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