Writer Adaptation for Online Handwriting Recognition
Identifieur interne : 002F19 ( Istex/Curation ); précédent : 002F18; suivant : 002F20Writer Adaptation for Online Handwriting Recognition
Auteurs : Anja Brakensiek [Allemagne] ; Andreas Kosmala [Allemagne] ; Gerhard Rigoll [Allemagne]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2001.
Abstract
Abstract: In this paper an on-line handwriting recognition system with focus on adaptation techniques is described. Our Hidden Markov Model (HMM) -based recognition system for cursive German script can be adapted to the writing style of a new writer using either a retraining depending on the maximum likelihood (ML)-approach or an adaptation according to the maximum a posteriori (MAP)-criterion. The performance of the resulting writer-dependent system increases significantly, even if only a few words are available for adaptation. So this approach is also applicable for on-line systems in hand-held computers such as PDAs. This paper deals with the performance comparison of two different adaptation techniques either in a supervised or an unsupervised mode with the availability of different amounts of adaptation data ranging from only 6 words up to 100 words per writer.
Url:
DOI: 10.1007/3-540-45404-7_5
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Anja Brakensiek<affiliation><mods:affiliation>Dept. of Computer Science, Faculty of Electrical Engineering, Gerhard-Mercator-University Duisburg, D-47057, Duisburg</mods:affiliation><wicri:noCountry code="subField">Duisburg</wicri:noCountry></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: anja@fb9-ti.uni-duisburg.de</mods:affiliation><country wicri:rule="url">Allemagne</country></affiliation><affiliation><mods:affiliation>Dept. of Computer Science, Faculty of Electrical Engineering, Gerhard-Mercator-University Duisburg, D-47057, Duisburg</mods:affiliation><wicri:noCountry code="subField">Duisburg</wicri:noCountry></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: kosmala@fb9-ti.uni-duisburg.de</mods:affiliation><country wicri:rule="url">Allemagne</country></affiliation><affiliation><mods:affiliation>Dept. of Computer Science, Faculty of Electrical Engineering, Gerhard-Mercator-University Duisburg, D-47057, Duisburg</mods:affiliation><wicri:noCountry code="subField">Duisburg</wicri:noCountry></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: rigoll@fb9-ti.uni-duisburg.de</mods:affiliation><country wicri:rule="url">Allemagne</country></affiliation>Le document en format XML
<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Writer Adaptation for Online Handwriting Recognition</title><author><name sortKey="Brakensiek, Anja" sort="Brakensiek, Anja" uniqKey="Brakensiek A" first="Anja" last="Brakensiek">Anja Brakensiek</name><affiliation><mods:affiliation>Dept. of Computer Science, Faculty of Electrical Engineering, Gerhard-Mercator-University Duisburg, D-47057, Duisburg</mods:affiliation><wicri:noCountry code="subField">Duisburg</wicri:noCountry></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: anja@fb9-ti.uni-duisburg.de</mods:affiliation><country wicri:rule="url">Allemagne</country></affiliation></author><author><name sortKey="Kosmala, Andreas" sort="Kosmala, Andreas" uniqKey="Kosmala A" first="Andreas" last="Kosmala">Andreas Kosmala</name><affiliation><mods:affiliation>Dept. of Computer Science, Faculty of Electrical Engineering, Gerhard-Mercator-University Duisburg, D-47057, Duisburg</mods:affiliation><wicri:noCountry code="subField">Duisburg</wicri:noCountry></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: kosmala@fb9-ti.uni-duisburg.de</mods:affiliation><country wicri:rule="url">Allemagne</country></affiliation></author><author><name sortKey="Rigoll, Gerhard" sort="Rigoll, Gerhard" uniqKey="Rigoll G" first="Gerhard" last="Rigoll">Gerhard Rigoll</name><affiliation><mods:affiliation>Dept. of Computer Science, Faculty of Electrical Engineering, Gerhard-Mercator-University Duisburg, D-47057, Duisburg</mods:affiliation><wicri:noCountry code="subField">Duisburg</wicri:noCountry></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: rigoll@fb9-ti.uni-duisburg.de</mods:affiliation><country wicri:rule="url">Allemagne</country></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:2EDDF26F892233B9A395C3F6BFBAC329F91967B1</idno><date when="2001" year="2001">2001</date><idno type="doi">10.1007/3-540-45404-7_5</idno><idno type="url">https://api.istex.fr/document/2EDDF26F892233B9A395C3F6BFBAC329F91967B1/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">003160</idno><idno type="wicri:Area/Istex/Curation">002F19</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Writer Adaptation for Online Handwriting Recognition</title><author><name sortKey="Brakensiek, Anja" sort="Brakensiek, Anja" uniqKey="Brakensiek A" first="Anja" last="Brakensiek">Anja Brakensiek</name><affiliation><mods:affiliation>Dept. of Computer Science, Faculty of Electrical Engineering, Gerhard-Mercator-University Duisburg, D-47057, Duisburg</mods:affiliation><wicri:noCountry code="subField">Duisburg</wicri:noCountry></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: anja@fb9-ti.uni-duisburg.de</mods:affiliation><country wicri:rule="url">Allemagne</country></affiliation></author><author><name sortKey="Kosmala, Andreas" sort="Kosmala, Andreas" uniqKey="Kosmala A" first="Andreas" last="Kosmala">Andreas Kosmala</name><affiliation><mods:affiliation>Dept. of Computer Science, Faculty of Electrical Engineering, Gerhard-Mercator-University Duisburg, D-47057, Duisburg</mods:affiliation><wicri:noCountry code="subField">Duisburg</wicri:noCountry></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: kosmala@fb9-ti.uni-duisburg.de</mods:affiliation><country wicri:rule="url">Allemagne</country></affiliation></author><author><name sortKey="Rigoll, Gerhard" sort="Rigoll, Gerhard" uniqKey="Rigoll G" first="Gerhard" last="Rigoll">Gerhard Rigoll</name><affiliation><mods:affiliation>Dept. of Computer Science, Faculty of Electrical Engineering, Gerhard-Mercator-University Duisburg, D-47057, Duisburg</mods:affiliation><wicri:noCountry code="subField">Duisburg</wicri:noCountry></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: rigoll@fb9-ti.uni-duisburg.de</mods:affiliation><country wicri:rule="url">Allemagne</country></affiliation></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Computer Science</title><imprint><date>2001</date></imprint><idno type="ISSN">0302-9743</idno><idno type="ISSN">0302-9743</idno></series><idno type="istex">2EDDF26F892233B9A395C3F6BFBAC329F91967B1</idno><idno type="DOI">10.1007/3-540-45404-7_5</idno><idno type="ChapterID">5</idno><idno type="ChapterID">Chap5</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: In this paper an on-line handwriting recognition system with focus on adaptation techniques is described. Our Hidden Markov Model (HMM) -based recognition system for cursive German script can be adapted to the writing style of a new writer using either a retraining depending on the maximum likelihood (ML)-approach or an adaptation according to the maximum a posteriori (MAP)-criterion. The performance of the resulting writer-dependent system increases significantly, even if only a few words are available for adaptation. So this approach is also applicable for on-line systems in hand-held computers such as PDAs. This paper deals with the performance comparison of two different adaptation techniques either in a supervised or an unsupervised mode with the availability of different amounts of adaptation data ranging from only 6 words up to 100 words per writer.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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