An Implementation of the Korean Sign Language Recognizer Using Neural Network Based on the Post PC
Identifieur interne : 004901 ( Istex/Corpus ); précédent : 004900; suivant : 004902An Implementation of the Korean Sign Language Recognizer Using Neural Network Based on the Post PC
Auteurs : Jung-Hyun Kim ; Kwang-Seok HongSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2006.
Abstract
Abstract: A traditional studies about recognition and representation technology of sign language have several restrictions such as conditionality in space and limitation of motion according to the technology of wire communication, problem of image capture system or video processing system for an acquisition of sign language signals, and the sign language recognition system based on word and morpheme. In order to overcome these restrictions and problems, in this paper, we implement the Korean sign language recognizer in the shape of sentence using neural network based on the Post wearable PC platform. The advantages of our approach are as follows: 1) it improves efficiency of the sign language input module according to the technology of wireless communication, 2) it recognizes and represents continuous sign language of users with flexibility in real time, and 3) it is possible more effective and free interchange of ideas and information between deaf person and hearing person (the public). Experimental result shows the average recognition rate of 92.8% about significant, dynamic and continuous the Korean sign language.
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DOI: 10.1007/11760023_33
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<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">An Implementation of the Korean Sign Language Recognizer Using Neural Network Based on the Post PC</title><author><name sortKey="Kim, Jung Hyun" sort="Kim, Jung Hyun" uniqKey="Kim J" first="Jung-Hyun" last="Kim">Jung-Hyun Kim</name><affiliation><mods:affiliation>School of Information and Communication Engineering, Sungkyunkwan University, 300, Chunchun-dong, Jangan-gu, 440-746, Suwon, KyungKi-do, Korea</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: kjh0328@skku.edu</mods:affiliation></affiliation></author><author><name sortKey="Hong, Kwang Seok" sort="Hong, Kwang Seok" uniqKey="Hong K" first="Kwang-Seok" last="Hong">Kwang-Seok Hong</name><affiliation><mods:affiliation>School of Information and Communication Engineering, Sungkyunkwan University, 300, Chunchun-dong, Jangan-gu, 440-746, Suwon, KyungKi-do, Korea</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: kshong@skku.ac.kr</mods:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:02B19BB5661D989C214C594373206AD3E5B89CDA</idno><date when="2006" year="2006">2006</date><idno type="doi">10.1007/11760023_33</idno><idno type="url">https://api.istex.fr/document/02B19BB5661D989C214C594373206AD3E5B89CDA/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">004901</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">An Implementation of the Korean Sign Language Recognizer Using Neural Network Based on the Post PC</title><author><name sortKey="Kim, Jung Hyun" sort="Kim, Jung Hyun" uniqKey="Kim J" first="Jung-Hyun" last="Kim">Jung-Hyun Kim</name><affiliation><mods:affiliation>School of Information and Communication Engineering, Sungkyunkwan University, 300, Chunchun-dong, Jangan-gu, 440-746, Suwon, KyungKi-do, Korea</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: kjh0328@skku.edu</mods:affiliation></affiliation></author><author><name sortKey="Hong, Kwang Seok" sort="Hong, Kwang Seok" uniqKey="Hong K" first="Kwang-Seok" last="Hong">Kwang-Seok Hong</name><affiliation><mods:affiliation>School of Information and Communication Engineering, Sungkyunkwan University, 300, Chunchun-dong, Jangan-gu, 440-746, Suwon, KyungKi-do, Korea</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: kshong@skku.ac.kr</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Computer Science</title><imprint><date>2006</date></imprint><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series><idno type="istex">02B19BB5661D989C214C594373206AD3E5B89CDA</idno><idno type="DOI">10.1007/11760023_33</idno><idno type="ChapterID">33</idno><idno type="ChapterID">Chap33</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: A traditional studies about recognition and representation technology of sign language have several restrictions such as conditionality in space and limitation of motion according to the technology of wire communication, problem of image capture system or video processing system for an acquisition of sign language signals, and the sign language recognition system based on word and morpheme. In order to overcome these restrictions and problems, in this paper, we implement the Korean sign language recognizer in the shape of sentence using neural network based on the Post wearable PC platform. The advantages of our approach are as follows: 1) it improves efficiency of the sign language input module according to the technology of wireless communication, 2) it recognizes and represents continuous sign language of users with flexibility in real time, and 3) it is possible more effective and free interchange of ideas and information between deaf person and hearing person (the public). 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In order to overcome these restrictions and problems, in this paper, we implement the Korean sign language recognizer in the shape of sentence using neural network based on the Post wearable PC platform. The advantages of our approach are as follows: 1) it improves efficiency of the sign language input module according to the technology of wireless communication, 2) it recognizes and represents continuous sign language of users with flexibility in real time, and 3) it is possible more effective and free interchange of ideas and information between deaf person and hearing person (the public). 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In order to overcome these restrictions and problems, in this paper, we implement the Korean sign language recognizer in the shape of sentence using neural network based on the Post wearable PC platform. The advantages of our approach are as follows: 1) it improves efficiency of the sign language input module according to the technology of wireless communication, 2) it recognizes and represents continuous sign language of users with flexibility in real time, and 3) it is possible more effective and free interchange of ideas and information between deaf person and hearing person (the public). 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ID="Aff9"><OrgName>Indian Institute of Technology</OrgName><OrgAddress><City>Madras</City><Country>India</Country></OrgAddress></Affiliation><Affiliation ID="Aff10"><OrgName>University of Dortmund</OrgName><OrgAddress><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff11"><OrgName>Massachusetts Institute of Technology</OrgName><OrgAddress><City>MA</City><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff12"><OrgName>University of California</OrgName><OrgAddress><City>Los Angeles</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff13"><OrgName>University of California</OrgName><OrgAddress><City>Berkeley</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff14"><OrgName>Rice University</OrgName><OrgAddress><City>Houston</City><State>TX</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff15"><OrgName>Max-Planck Institute of Computer Science</OrgName><OrgAddress><City>Saarbruecken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SeriesHeader><Book Language="En"><BookInfo BookProductType="Proceedings" ContainsESM="No" Language="En" MediaType="eBook" NumberingDepth="2" NumberingStyle="ContentOnly" OutputMedium="All" TocLevels="0"><BookID>978-3-540-34438-4</BookID><BookTitle>Advances in Neural Networks - ISNN 2006</BookTitle><BookSubTitle>Third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006, Proceedings, Part II</BookSubTitle><BookVolumeNumber>3972</BookVolumeNumber><BookSequenceNumber>3972</BookSequenceNumber><BookDOI>10.1007/11760023</BookDOI><BookTitleID>139129</BookTitleID><BookPrintISBN>978-3-540-34437-7</BookPrintISBN><BookElectronicISBN>978-3-540-34438-4</BookElectronicISBN><BookChapterCount>205</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2006</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I16013" Priority="1" Type="Secondary">Computation by Abstract Devices</BookSubject><BookSubject Code="I13022" Priority="2" Type="Secondary">Computer Communication Networks</BookSubject><BookSubject Code="I16021" Priority="3" Type="Secondary">Algorithm Analysis and Problem Complexity</BookSubject><BookSubject Code="I17028" Priority="4" Type="Secondary">Discrete Mathematics in Computer Science</BookSubject><BookSubject Code="I21017" Priority="5" Type="Secondary">Artificial Intelligence (incl. Robotics)</BookSubject><BookSubject Code="I2203X" Priority="6" Type="Secondary">Pattern Recognition</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID></BookContext></BookInfo><BookHeader><EditorGroup><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>Jun</GivenName><FamilyName>Wang</FamilyName></EditorName><Contact><Email>jwang@mae.cuhk.edu.hk</Email></Contact></Editor><Editor AffiliationIDS="Aff17"><EditorName DisplayOrder="Western"><GivenName>Zhang</GivenName><FamilyName>Yi</FamilyName></EditorName><Contact><Email>zhangyi@uestc.edu.cn</Email></Contact></Editor><Editor AffiliationIDS="Aff18"><EditorName DisplayOrder="Western"><GivenName>Jacek</GivenName><GivenName>M.</GivenName><FamilyName>Zurada</FamilyName></EditorName><Contact><Email>j.zurada@ieee.org</Email></Contact></Editor><Editor AffiliationIDS="Aff19"><EditorName DisplayOrder="Western"><GivenName>Bao-Liang</GivenName><FamilyName>Lu</FamilyName></EditorName><Contact><Email>bllu@sjtu.edu.cn</Email></Contact></Editor><Editor AffiliationIDS="Aff20"><EditorName DisplayOrder="Western"><GivenName>Hujun</GivenName><FamilyName>Yin</FamilyName></EditorName><Contact><Email>h.yin@manchester.ac.uk</Email></Contact></Editor><Affiliation ID="Aff16"><OrgDivision>Department of Mechanical and Automation Engineering</OrgDivision><OrgName>The Chinese University of Hong Kong</OrgName><OrgAddress><Street>Shatin, New Territories</Street><City>Hong Kong</City><Country>China</Country></OrgAddress></Affiliation><Affiliation ID="Aff17"><OrgDivision>Computational Intelligence Laboratory, School of Computer Science and Engineering</OrgDivision><OrgName>University of Electronic Science and Technology of China</OrgName><OrgAddress><Postcode>610054</Postcode><City>Chengdu</City><Country>P.R. China</Country></OrgAddress></Affiliation><Affiliation ID="Aff18"><OrgDivision>Department of Electrical Engineering</OrgDivision><OrgName>University of Louisville</OrgName><OrgAddress><Postcode>40292</Postcode><City>Louisville</City><State>KY</State><Country>U.S.A</Country></OrgAddress></Affiliation><Affiliation ID="Aff19"><OrgDivision>Laboratory for Computational Biology</OrgDivision><OrgName>Shanghai Center for Systems Biomedicine</OrgName><OrgAddress><Street>800 Dong Chuan Rd.</Street><Postcode>200240</Postcode><City>Shanghai</City><Country>China</Country></OrgAddress></Affiliation><Affiliation ID="Aff20"><OrgDivision>School of Electrical and Electronic Engineering</OrgDivision><OrgName>University of Manchester</OrgName><OrgAddress><Country>UK</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Part ID="Part1"><PartInfo TocLevels="0"><PartID>1</PartID><PartSequenceNumber>1</PartSequenceNumber><PartTitle>Pattern Classification</PartTitle><PartChapterCount>54</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>Advances in Neural Networks - ISNN 2006</BookTitle></PartContext></PartInfo><Chapter ID="Chap33" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>33</ChapterID><ChapterDOI>10.1007/11760023_33</ChapterDOI><ChapterSequenceNumber>33</ChapterSequenceNumber><ChapterTitle Language="En">An Implementation of the Korean Sign Language Recognizer Using Neural Network Based on the Post PC</ChapterTitle><ChapterFirstPage>222</ChapterFirstPage><ChapterLastPage>231</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2006</CopyrightYear></ChapterCopyright><ChapterGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant Grant="Restricted"></BibliographyGrant><ESMGrant Grant="Restricted"></ESMGrant></ChapterGrants><ChapterContext><SeriesID>558</SeriesID><PartID>1</PartID><BookID>978-3-540-34438-4</BookID><BookTitle>Advances in Neural Networks - ISNN 2006</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff21"><AuthorName DisplayOrder="Western"><GivenName>Jung-Hyun</GivenName><FamilyName>Kim</FamilyName></AuthorName><Contact><Email>kjh0328@skku.edu</Email><URL>http://hci.skku.ac.kr</URL></Contact></Author><Author AffiliationIDS="Aff21"><AuthorName DisplayOrder="Western"><GivenName>Kwang-Seok</GivenName><FamilyName>Hong</FamilyName></AuthorName><Contact><Email>kshong@skku.ac.kr</Email><URL>http://hci.skku.ac.kr</URL></Contact></Author><Affiliation ID="Aff21"><OrgDivision>School of Information and Communication Engineering</OrgDivision><OrgName>Sungkyunkwan University</OrgName><OrgAddress><Street>300, Chunchun-dong, Jangan-gu</Street><City>Suwon, KyungKi-do</City><Postcode>440-746</Postcode><Country>Korea</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>A traditional studies about recognition and representation technology of sign language have several restrictions such as conditionality in space and limitation of motion according to the technology of wire communication, problem of image capture system or video processing system for an acquisition of sign language signals, and the sign language recognition system based on word and morpheme. In order to overcome these restrictions and problems, in this paper, we implement the Korean sign language recognizer in the shape of sentence using neural network based on the Post wearable PC platform. The advantages of our approach are as follows: 1) it improves efficiency of the sign language input module according to the technology of wireless communication, 2) it recognizes and represents continuous sign language of users with flexibility in real time, and 3) it is possible more effective and free interchange of ideas and information between deaf person and hearing person (the public). Experimental result shows the average recognition rate of 92.8% about significant, dynamic and continuous the Korean sign language.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>An Implementation of the Korean Sign Language Recognizer Using Neural Network Based on the Post PC</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>An Implementation of the Korean Sign Language Recognizer Using Neural Network Based on the Post PC</title></titleInfo><name type="personal"><namePart type="given">Jung-Hyun</namePart><namePart type="family">Kim</namePart><affiliation>School of Information and Communication Engineering, Sungkyunkwan University, 300, Chunchun-dong, Jangan-gu, 440-746, Suwon, KyungKi-do, Korea</affiliation><affiliation>E-mail: kjh0328@skku.edu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Kwang-Seok</namePart><namePart type="family">Hong</namePart><affiliation>School of Information and Communication Engineering, Sungkyunkwan University, 300, Chunchun-dong, Jangan-gu, 440-746, Suwon, KyungKi-do, Korea</affiliation><affiliation>E-mail: kshong@skku.ac.kr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference [eBooks]" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2006</dateIssued><copyrightDate encoding="w3cdtf">2006</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Abstract: A traditional studies about recognition and representation technology of sign language have several restrictions such as conditionality in space and limitation of motion according to the technology of wire communication, problem of image capture system or video processing system for an acquisition of sign language signals, and the sign language recognition system based on word and morpheme. In order to overcome these restrictions and problems, in this paper, we implement the Korean sign language recognizer in the shape of sentence using neural network based on the Post wearable PC platform. The advantages of our approach are as follows: 1) it improves efficiency of the sign language input module according to the technology of wireless communication, 2) it recognizes and represents continuous sign language of users with flexibility in real time, and 3) it is possible more effective and free interchange of ideas and information between deaf person and hearing person (the public). Experimental result shows the average recognition rate of 92.8% about significant, dynamic and continuous the Korean sign language.</abstract><relatedItem type="host"><titleInfo><title>Advances in Neural Networks - ISNN 2006</title><subTitle>Third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006, Proceedings, Part II</subTitle></titleInfo><name type="personal"><namePart type="given">Jun</namePart><namePart type="family">Wang</namePart><affiliation>Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China</affiliation><affiliation>E-mail: jwang@mae.cuhk.edu.hk</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Zhang</namePart><namePart type="family">Yi</namePart><affiliation>Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China</affiliation><affiliation>E-mail: zhangyi@uestc.edu.cn</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jacek</namePart><namePart type="given">M.</namePart><namePart type="family">Zurada</namePart><affiliation>Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A</affiliation><affiliation>E-mail: j.zurada@ieee.org</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Bao-Liang</namePart><namePart type="family">Lu</namePart><affiliation>Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China</affiliation><affiliation>E-mail: bllu@sjtu.edu.cn</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Hujun</namePart><namePart type="family">Yin</namePart><affiliation>School of Electrical and Electronic Engineering, University of Manchester, UK</affiliation><affiliation>E-mail: h.yin@manchester.ac.uk</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="Book Series" displayLabel="Proceedings"></genre><originInfo><copyrightDate encoding="w3cdtf">2006</copyrightDate><issuance>monographic</issuance></originInfo><subject><genre>Book Subject Collection</genre><topic authority="SpringerSubjectCodes" authorityURI="SUCO11645">Computer Science</topic></subject><subject><genre>Book Subject Group</genre><topic authority="SpringerSubjectCodes" authorityURI="I">Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="I16013">Computation by Abstract Devices</topic><topic authority="SpringerSubjectCodes" authorityURI="I13022">Computer Communication Networks</topic><topic authority="SpringerSubjectCodes" authorityURI="I16021">Algorithm Analysis and Problem Complexity</topic><topic authority="SpringerSubjectCodes" authorityURI="I17028">Discrete Mathematics in Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="I21017">Artificial Intelligence (incl. 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2006</recordOrigin></recordInfo></relatedItem><relatedItem type="series"><titleInfo><title>Lecture Notes in Computer Science</title></titleInfo><name type="personal"><namePart type="given">David</namePart><namePart type="family">Hutchison</namePart><affiliation>Lancaster University, UK</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Takeo</namePart><namePart type="family">Kanade</namePart><affiliation>Carnegie Mellon University, Pittsburgh, PA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Josef</namePart><namePart type="family">Kittler</namePart><affiliation>University of Surrey, Guildford, UK</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jon</namePart><namePart type="given">M.</namePart><namePart type="family">Kleinberg</namePart><affiliation>Cornell University, Ithaca, NY, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Friedemann</namePart><namePart type="family">Mattern</namePart><affiliation>ETH Zurich, Switzerland</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">John</namePart><namePart type="given">C.</namePart><namePart type="family">Mitchell</namePart><affiliation>Stanford University, CA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Moni</namePart><namePart type="family">Naor</namePart><affiliation>Weizmann Institute of Science, Rehovot, Israel</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Oscar</namePart><namePart type="family">Nierstrasz</namePart><affiliation>University of Bern, Switzerland</affiliation><role><roleTerm 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