Classification de formulaires manuscrits en-ligne à l'aide de réseaux bayésiens
Identifieur interne : 002D92 ( Main/Exploration ); précédent : 002D91; suivant : 002D93Classification de formulaires manuscrits en-ligne à l'aide de réseaux bayésiens
Auteurs : Emilie Philippot [France] ; Yolande Belaïd [France] ; Abdel Belaïd [France]Source :
Descripteurs français
Abstract
In this paper, we propose a method of recognizing handwritten forms online using an electronic pen-type clip. This mode of writing leads unconstrained filling uncertain form and the loss of the type of the completed form. We propose a method to identify the class of the form based on the positioning of fields filled. Our approach relies on the use of Bayesian networks to exploit, using conditional probabilities, occurrences of filling some fields depending on others. The tests were conducted on the basis of 3200 forms provided by the company Actimage. The first experiments show an average recognition of 90%.
Url:
Affiliations:
- France
- Grand Est, Lorraine (région)
- Nancy
- Institut national polytechnique de Lorraine, Université Nancy 2, Université de Lorraine
Links toward previous steps (curation, corpus...)
- to stream Hal, to step Corpus: 005810
- to stream Hal, to step Curation: 005810
- to stream Hal, to step Checkpoint: 002657
- to stream Main, to step Merge: 002E59
- to stream Main, to step Curation: 002D92
Le document en format XML
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1</orgName><orgName type="acronym">UHP</orgName><date type="end">2011-12-31</date><desc><address><addrLine>24-30 rue Lionnois, BP 60120, 54 003 NANCY cedex, France</addrLine><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-300292" type="indirect"><org type="institution" xml:id="struct-300292" status="OLD"><orgName>Université Nancy 2</orgName><date type="end">2011-12-31</date><desc><address><addrLine>91 avenue de la Libération, BP 454, 54001 Nancy cedex</addrLine><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-300293" type="indirect"><org type="institution" xml:id="struct-300293" status="OLD"><orgName>Institut National Polytechnique de Lorraine</orgName><orgName type="acronym">INPL</orgName><date type="end">2011-12-31</date><desc><address><country key="FR"></country></address></desc></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université Nancy 2</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Institut national polytechnique de Lorraine</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="fr"><term>Formulaires</term><term>classification</term><term>réseaux bayésiens</term><term>écriture manuscrite en-ligne</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In this paper, we propose a method of recognizing handwritten forms online using an electronic pen-type clip. This mode of writing leads unconstrained filling uncertain form and the loss of the type of the completed form. We propose a method to identify the class of the form based on the positioning of fields filled. Our approach relies on the use of Bayesian networks to exploit, using conditional probabilities, occurrences of filling some fields depending on others. The tests were conducted on the basis of 3200 forms provided by the company Actimage. The first experiments show an average recognition of 90%.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Nancy</li></settlement><orgName><li>Institut national polytechnique de Lorraine</li><li>Université Nancy 2</li><li>Université de Lorraine</li></orgName></list><tree><country name="France"><region name="Grand Est"><name sortKey="Philippot, Emilie" sort="Philippot, Emilie" uniqKey="Philippot E" first="Emilie" last="Philippot">Emilie Philippot</name></region><name sortKey="Belaid, Abdel" sort="Belaid, Abdel" uniqKey="Belaid A" first="Abdel" last="Belaïd">Abdel Belaïd</name><name sortKey="Belaid, Yolande" sort="Belaid, Yolande" uniqKey="Belaid Y" first="Yolande" last="Belaïd">Yolande Belaïd</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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