Bayesian networks for incomplete data analysis in form processing
Identifieur interne : 000C78 ( Main/Exploration ); précédent : 000C77; suivant : 000C79Bayesian networks for incomplete data analysis in form processing
Auteurs : Emilie Philippot [France] ; Santosh K. C. [France] ; Abdel Belaïd [France] ; Yolande Belaïd [France]Source :
English descriptors
Abstract
In this paper, we study Bayesian network (BN) for form identification based on partially filled fields. It uses electronic ink-tracing files without having any information about form structure. Given a form format, the ink-tracing files are used to build the BN by providing the possible relationships between corresponding fields using conditional probabilities, that goes from individual fields up to the complete model construction. To simplify the BN, we sub-divide a single form into three different areas: header, body and footer, and integrate them together, where we study three fundamental BN learning algorithms: Naive, Peter & Clark (PC) and maximum weighted spanning tree (MWST). Under this framework, we validate it with a real-world industrial problem i.e., electronic note-taking in form processing. The approach provides satisfactory results, attesting the interest of BN for exploiting the incomplete form analysis problems, in particular.
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DOI: 10.1007/s13042-014-0234-4
Affiliations:
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BP 10578153 Le Chesnay Cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.inria.fr/en/</ref></desc></org></tutelle><tutelle active="#struct-413289" type="indirect"><org type="institution" xml:id="struct-413289" status="VALID"><idno type="IdRef">157040569</idno><idno type="IdUnivLorraine">[UL]100--</idno><orgName>Université de Lorraine</orgName><orgName type="acronym">UL</orgName><date type="start">2012-01-01</date><desc><address><addrLine>34 cours Léopold - CS 25233 - 54052 Nancy cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.univ-lorraine.fr/</ref></desc></org></tutelle><tutelle name="UMR7503" active="#struct-441569" type="indirect"><org type="institution" xml:id="struct-441569" status="VALID"><idno type="ISNI">0000000122597504</idno><idno type="IdRef">02636817X</idno><orgName>Centre National de la Recherche Scientifique</orgName><orgName type="acronym">CNRS</orgName><date type="start">1939-10-19</date><desc><address><country key="FR"></country></address><ref type="url">http://www.cnrs.fr/</ref></desc></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><settlement type="city">Metz</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université de Lorraine</orgName></affiliation></author><author><name sortKey="Belaid, Yolande" sort="Belaid, Yolande" uniqKey="Belaid Y" first="Yolande" last="Belaïd">Yolande Belaïd</name><affiliation wicri:level="1"><hal:affiliation type="researchteam" xml:id="struct-206042" status="VALID"><orgName>Recognition of writing and analysis of documents</orgName><orgName type="acronym">READ</orgName><desc><address><country key="FR"></country></address></desc><listRelation><relation active="#struct-423086" type="direct"></relation><relation active="#struct-206040" type="indirect"></relation><relation active="#struct-300009" type="indirect"></relation><relation active="#struct-413289" type="indirect"></relation><relation name="UMR7503" active="#struct-441569" type="indirect"></relation></listRelation><tutelles><tutelle active="#struct-423086" type="direct"><org type="department" xml:id="struct-423086" status="VALID"><orgName>Department of Natural Language Processing & Knowledge Discovery</orgName><orgName type="acronym">LORIA - NLPKD</orgName><desc><address><country key="FR"></country></address><ref type="url">http://www.loria.fr/la-recherche-en/departements/Knowledge-and-Language-Management</ref></desc><listRelation><relation active="#struct-206040" type="direct"></relation><relation active="#struct-300009" type="indirect"></relation><relation active="#struct-413289" type="indirect"></relation><relation name="UMR7503" active="#struct-441569" type="indirect"></relation></listRelation></org></tutelle><tutelle active="#struct-206040" type="indirect"><org type="laboratory" xml:id="struct-206040" status="VALID"><idno type="IdRef">067077927</idno><idno type="RNSR">198912571S</idno><idno type="IdUnivLorraine">[UL]RSI--</idno><orgName>Laboratoire Lorrain de Recherche en Informatique et ses Applications</orgName><orgName type="acronym">LORIA</orgName><date type="start">2012-01-01</date><desc><address><addrLine>Campus Scientifique BP 239 54506 Vandoeuvre-lès-Nancy Cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.loria.fr</ref></desc><listRelation><relation active="#struct-300009" type="direct"></relation><relation active="#struct-413289" type="direct"></relation><relation name="UMR7503" active="#struct-441569" type="direct"></relation></listRelation></org></tutelle><tutelle active="#struct-300009" type="indirect"><org type="institution" xml:id="struct-300009" status="VALID"><orgName>Institut National de Recherche en Informatique et en Automatique</orgName><orgName type="acronym">Inria</orgName><desc><address><addrLine>Domaine de VoluceauRocquencourt - BP 10578153 Le Chesnay Cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.inria.fr/en/</ref></desc></org></tutelle><tutelle active="#struct-413289" type="indirect"><org type="institution" xml:id="struct-413289" status="VALID"><idno type="IdRef">157040569</idno><idno type="IdUnivLorraine">[UL]100--</idno><orgName>Université de Lorraine</orgName><orgName type="acronym">UL</orgName><date type="start">2012-01-01</date><desc><address><addrLine>34 cours Léopold - CS 25233 - 54052 Nancy cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.univ-lorraine.fr/</ref></desc></org></tutelle><tutelle name="UMR7503" active="#struct-441569" type="indirect"><org type="institution" xml:id="struct-441569" status="VALID"><idno type="ISNI">0000000122597504</idno><idno type="IdRef">02636817X</idno><orgName>Centre National de la Recherche Scientifique</orgName><orgName type="acronym">CNRS</orgName><date type="start">1939-10-19</date><desc><address><country key="FR"></country></address><ref type="url">http://www.cnrs.fr/</ref></desc></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><settlement type="city">Metz</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université de Lorraine</orgName></affiliation></author></analytic><idno type="DOI">10.1007/s13042-014-0234-4</idno></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>Bayesian Networks</term><term>Electronic Note-taking</term><term>Form Processing</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In this paper, we study Bayesian network (BN) for form identification based on partially filled fields. It uses electronic ink-tracing files without having any information about form structure. Given a form format, the ink-tracing files are used to build the BN by providing the possible relationships between corresponding fields using conditional probabilities, that goes from individual fields up to the complete model construction. To simplify the BN, we sub-divide a single form into three different areas: header, body and footer, and integrate them together, where we study three fundamental BN learning algorithms: Naive, Peter & Clark (PC) and maximum weighted spanning tree (MWST). Under this framework, we validate it with a real-world industrial problem i.e., electronic note-taking in form processing. The approach provides satisfactory results, attesting the interest of BN for exploiting the incomplete form analysis problems, in particular.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Metz</li><li>Nancy</li></settlement><orgName><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><name sortKey="K C, Santosh" sort="K C, Santosh" uniqKey="K C S" first="Santosh" last="K. C.">Santosh K. C.</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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