Optimal design of reference models for large-set handwritten character recognition
Identifieur interne : 002C75 ( Main/Exploration ); précédent : 002C74; suivant : 002C76Optimal design of reference models for large-set handwritten character recognition
Auteurs : Seong-Whan Lee [Corée du Sud] ; Hee-Heon Song [Corée du Sud]Source :
- Pattern Recognition [ 0031-3203 ] ; 1994.
Abstract
For the recognition of large-set handwritten characters, classification methods based on pattern matching have been commonly used, and good reference models play an important role in achieving high performance in these methods. Learning Vector Quantization (LVQ) has been intensively studied to generate good reference models in speech recognition since 1986. However, the design of reference models based on LVQ has several drawbacks for the recognition of large-set handwritten characters.In this paper, to cope with these, we propose a new method for the optimal design of reference models using Simulated Annealing combined with an improved LVQ3 for the recognition of large-set handwritten characters. Experimental results reveal that the proposed method is superior to the conventional method based on averaging or other LVQ-based methods.
Url:
DOI: 10.1016/0031-3203(94)90010-8
Affiliations:
Links toward previous steps (curation, corpus...)
- to stream Istex, to step Corpus: 002494
- to stream Istex, to step Curation: 002329
- to stream Istex, to step Checkpoint: 001F70
- to stream Main, to step Merge: 002E42
- to stream Main, to step Curation: 002C75
Le document en format XML
<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title>Optimal design of reference models for large-set handwritten character recognition</title>
<author><name sortKey="Lee, Seong Whan" sort="Lee, Seong Whan" uniqKey="Lee S" first="Seong-Whan" last="Lee">Seong-Whan Lee</name>
</author>
<author><name sortKey="Song, Hee Heon" sort="Song, Hee Heon" uniqKey="Song H" first="Hee-Heon" last="Song">Hee-Heon Song</name>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">ISTEX</idno>
<idno type="RBID">ISTEX:07750FA9A821FD4CE8FF0E28A3594AFBA9457DCC</idno>
<date when="1994" year="1994">1994</date>
<idno type="doi">10.1016/0031-3203(94)90010-8</idno>
<idno type="url">https://api.istex.fr/document/07750FA9A821FD4CE8FF0E28A3594AFBA9457DCC/fulltext/pdf</idno>
<idno type="wicri:Area/Istex/Corpus">002494</idno>
<idno type="wicri:Area/Istex/Curation">002329</idno>
<idno type="wicri:Area/Istex/Checkpoint">001F70</idno>
<idno type="wicri:doubleKey">0031-3203:1994:Lee S:optimal:design:of</idno>
<idno type="wicri:Area/Main/Merge">002E42</idno>
<idno type="wicri:Area/Main/Curation">002C75</idno>
<idno type="wicri:Area/Main/Exploration">002C75</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title level="a">Optimal design of reference models for large-set handwritten character recognition</title>
<author><name sortKey="Lee, Seong Whan" sort="Lee, Seong Whan" uniqKey="Lee S" first="Seong-Whan" last="Lee">Seong-Whan Lee</name>
<affiliation wicri:level="1"><country xml:lang="fr">Corée du Sud</country>
<wicri:regionArea>Department of Computer Science, Chungbuk National University, San 48 Gaeshindong, Cheongju, Chungbuk 360-763</wicri:regionArea>
<wicri:noRegion>Chungbuk 360-763</wicri:noRegion>
</affiliation>
</author>
<author><name sortKey="Song, Hee Heon" sort="Song, Hee Heon" uniqKey="Song H" first="Hee-Heon" last="Song">Hee-Heon Song</name>
<affiliation wicri:level="1"><country xml:lang="fr">Corée du Sud</country>
<wicri:regionArea>Department of Computer Science, Chungbuk National University, San 48 Gaeshindong, Cheongju, Chungbuk 360-763</wicri:regionArea>
<wicri:noRegion>Chungbuk 360-763</wicri:noRegion>
</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="1994">1994</date>
<biblScope unit="volume">27</biblScope>
<biblScope unit="issue">9</biblScope>
<biblScope unit="page" from="1267">1267</biblScope>
<biblScope unit="page" to="1274">1274</biblScope>
</imprint>
<idno type="ISSN">0031-3203</idno>
</series>
<idno type="istex">07750FA9A821FD4CE8FF0E28A3594AFBA9457DCC</idno>
<idno type="DOI">10.1016/0031-3203(94)90010-8</idno>
<idno type="PII">0031-3203(94)90010-8</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">For the recognition of large-set handwritten characters, classification methods based on pattern matching have been commonly used, and good reference models play an important role in achieving high performance in these methods. Learning Vector Quantization (LVQ) has been intensively studied to generate good reference models in speech recognition since 1986. However, the design of reference models based on LVQ has several drawbacks for the recognition of large-set handwritten characters.In this paper, to cope with these, we propose a new method for the optimal design of reference models using Simulated Annealing combined with an improved LVQ3 for the recognition of large-set handwritten characters. Experimental results reveal that the proposed method is superior to the conventional method based on averaging or other LVQ-based methods.</div>
</front>
</TEI>
<affiliations><list><country><li>Corée du Sud</li>
</country>
</list>
<tree><country name="Corée du Sud"><noRegion><name sortKey="Lee, Seong Whan" sort="Lee, Seong Whan" uniqKey="Lee S" first="Seong-Whan" last="Lee">Seong-Whan Lee</name>
</noRegion>
<name sortKey="Song, Hee Heon" sort="Song, Hee Heon" uniqKey="Song H" first="Hee-Heon" last="Song">Hee-Heon Song</name>
</country>
</tree>
</affiliations>
</record>
Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Ticri/CIDE/explor/OcrV1/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 002C75 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 002C75 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien |wiki= Ticri/CIDE |area= OcrV1 |flux= Main |étape= Exploration |type= RBID |clé= ISTEX:07750FA9A821FD4CE8FF0E28A3594AFBA9457DCC |texte= Optimal design of reference models for large-set handwritten character recognition }}
This area was generated with Dilib version V0.6.32. |