A System for Indian Postal Automation
Identifieur interne : 004519 ( Hal/Checkpoint ); précédent : 004518; suivant : 004520A System for Indian Postal Automation
Auteurs : Kaushik Roy [Inde] ; Szilárd Vajda [France] ; Umapada Pal [Inde] ; Bidyut Baran Chaudhuri [Inde] ; Abdel Belaïd [France]Source :
Abstract
In this paper, we present a system towards Indian postal automation based on the recognition of pin-code and city name of the postal document. In the proposed system, at first, non-text blocks (postal stamp, postal seal etc.) are detected and Destination Address Block (DAB) is identified from the document. Next, lines and words of the DAB are segmented. Since India is a multi-lingual and multi-script country, the address part may be written by combination of two scripts. To identify the script by which a word is written, we propose a water reservoir based technique. It is very difficult to identify the script by which the pin-code portion is written. So we have used two-stage artificial Neural Network (NN) based general classifiers for the recognition of pin-code digits written in English/Bangla. For recognition of city names we propose an NSHP-HMM (Non-Symmetric Half Plane-Hidden Markov Model) based technique. At present, the accuracy of the digit recognition module is 93.14% while that of city name recognition scheme is about 86.44%.
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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><author><name sortKey="Pal, Umapada" sort="Pal, Umapada" uniqKey="Pal U" first="Umapada" last="Pal">Umapada Pal</name><affiliation wicri:level="1"><hal:affiliation type="laboratory" xml:id="struct-7209" 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type="direct"></relation></listRelation></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><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-300291" type="indirect"><org type="institution" xml:id="struct-300291" status="OLD"><orgName>Université Henri Poincaré - Nancy 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></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In this paper, we present a system towards Indian postal automation based on the recognition of pin-code and city name of the postal document. In the proposed system, at first, non-text blocks (postal stamp, postal seal etc.) are detected and Destination Address Block (DAB) is identified from the document. Next, lines and words of the DAB are segmented. Since India is a multi-lingual and multi-script country, the address part may be written by combination of two scripts. To identify the script by which a word is written, we propose a water reservoir based technique. It is very difficult to identify the script by which the pin-code portion is written. So we have used two-stage artificial Neural Network (NN) based general classifiers for the recognition of pin-code digits written in English/Bangla. For recognition of city names we propose an NSHP-HMM (Non-Symmetric Half Plane-Hidden Markov Model) based technique. At present, the accuracy of the digit recognition module is 93.14% while that of city name recognition scheme is about 86.44%.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">A System for Indian Postal Automation</title><author role="aut"><persName><forename type="first">Kaushik</forename><surname>Roy</surname></persName><email></email><idno type="halauthor">52803</idno><affiliation ref="#struct-7209"></affiliation></author><author role="aut"><persName><forename type="first">Szilárd</forename><surname>Vajda</surname></persName><email>vajda@loria.fr</email><idno type="halauthor">52833</idno><affiliation ref="#struct-2362"></affiliation></author><author role="aut"><persName><forename type="first">Umapada</forename><surname>Pal</surname></persName><email></email><idno type="halauthor">52805</idno><affiliation ref="#struct-7209"></affiliation></author><author role="aut"><persName><forename type="first">Bidyut</forename><surname>Baran Chaudhuri</surname></persName><email></email><idno type="halauthor">52806</idno><affiliation ref="#struct-7209"></affiliation></author><author role="aut"><persName><forename type="first">Abdel</forename><surname>Belaïd</surname></persName><email></email><idno type="halauthor">52807</idno><affiliation ref="#struct-2362"></affiliation></author><editor role="depositor"><persName><forename>Szilard</forename><surname>Vajda</surname></persName><email>vajda@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2006-04-20 13:31:01</date><date type="whenModified">2016-05-19 01:05:40</date><date type="whenReleased">2006-04-21 15:18:51</date><date type="whenProduced">2005-09-29</date><date type="whenEndEmbargoed">2006-04-20</date><ref type="file" target="https://hal.inria.fr/inria-00000364/document"><date notBefore="2006-04-20"></date></ref><ref type="file" n="1" target="https://hal.inria.fr/inria-00000364/file/ICDAR05_vajda1.pdf"><date notBefore="2006-04-20"></date></ref></edition><respStmt><resp>contributor</resp><name key="102580"><persName><forename>Szilard</forename><surname>Vajda</surname></persName><email>vajda@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">inria-00000364</idno><idno type="halUri">https://hal.inria.fr/inria-00000364</idno><idno type="halBibtex">roy:inria-00000364</idno><idno type="halRefHtml">International Conference on Document Analysis and Recognition, Sep 2005, Seoul, Korea, 2005</idno><idno type="halRef">International Conference on Document Analysis and Recognition, Sep 2005, Seoul, Korea, 2005</idno></publicationStmt><seriesStmt><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="LABO-LORIA-SET" p="LORIA">LABO-LORIA-SET</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="LORIA-TALC" p="LORIA">Traitement automatique des langues et des connaissances</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno><idno type="stamp" n="INPL">Institut National Polytechnique de Lorraine</idno></seriesStmt><notesStmt><note type="audience" n="1">Not set</note><note type="invited" n="0">No</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note><note type="proceedings" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">A System for Indian Postal Automation</title><author role="aut"><persName><forename type="first">Kaushik</forename><surname>Roy</surname></persName><idno type="halAuthorId">52803</idno><affiliation ref="#struct-7209"></affiliation></author><author role="aut"><persName><forename type="first">Szilárd</forename><surname>Vajda</surname></persName><email>vajda@loria.fr</email><idno type="halAuthorId">52833</idno><affiliation ref="#struct-2362"></affiliation></author><author role="aut"><persName><forename type="first">Umapada</forename><surname>Pal</surname></persName><idno type="halAuthorId">52805</idno><affiliation ref="#struct-7209"></affiliation></author><author role="aut"><persName><forename type="first">Bidyut</forename><surname>Baran Chaudhuri</surname></persName><idno type="halAuthorId">52806</idno><affiliation ref="#struct-7209"></affiliation></author><author role="aut"><persName><forename type="first">Abdel</forename><surname>Belaïd</surname></persName><idno type="halAuthorId">52807</idno><affiliation ref="#struct-2362"></affiliation></author></analytic><monogr><meeting><title>International Conference on Document Analysis and Recognition</title><date type="start">2005-09-29</date><settlement>Seoul, Korea</settlement></meeting><imprint><date type="datePub">2005-09-29</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><classCode scheme="halDomain" n="info.info-ai">Computer Science [cs]/Artificial Intelligence [cs.AI]</classCode><classCode scheme="halDomain" n="info.info-cv">Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]</classCode><classCode scheme="halDomain" n="info.info-lg">Computer Science [cs]/Machine Learning [cs.LG]</classCode><classCode scheme="halDomain" n="info.info-ne">Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="en">In this paper, we present a system towards Indian postal automation based on the recognition of pin-code and city name of the postal document. In the proposed system, at first, non-text blocks (postal stamp, postal seal etc.) are detected and Destination Address Block (DAB) is identified from the document. Next, lines and words of the DAB are segmented. Since India is a multi-lingual and multi-script country, the address part may be written by combination of two scripts. To identify the script by which a word is written, we propose a water reservoir based technique. It is very difficult to identify the script by which the pin-code portion is written. So we have used two-stage artificial Neural Network (NN) based general classifiers for the recognition of pin-code digits written in English/Bangla. For recognition of city names we propose an NSHP-HMM (Non-Symmetric Half Plane-Hidden Markov Model) based technique. At present, the accuracy of the digit recognition module is 93.14% while that of city name recognition scheme is about 86.44%.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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