untitled
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<identifier>oai:HAL:hal-01165923v1</identifier>
<datestamp>2015-06-22</datestamp>
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<setSpec>subject:info</setSpec>
<setSpec>collection:UNIV-AG</setSpec>
<setSpec>collection:IGS2015</setSpec>
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<metadata><dc>
<publisher>HAL CCSD</publisher>
<title lang=en>The generation of synthetic handwritten data for improving on-line learning</title>
<creator>Režnáková, Marta</creator>
<creator>Tencer, Lukas</creator>
<creator>Plamondon, Réjean</creator>
<creator>Cheriet, Mohamed</creator>
<contributor>Ecole de Technologie Supérieure [Montréal] (ETS)</contributor>
<contributor>Laboratoire scribens ; Ecole Polytechnique de Montréal (EPM)</contributor>
<description>International audience</description>
<source>17th Biennial Conference of the International Graphonomics Society</source>
<coverage>Pointe-à-Pitre, Guadeloupe</coverage>
<contributor>International Graphonomics Society (IGS)</contributor>
<contributor>Université des Antilles (UA)</contributor>
<contributor>Céline Rémi</contributor>
<contributor>Lionel Prévost</contributor>
<contributor>Eric Anquetil</contributor>
<identifier>hal-01165923</identifier>
<identifier>https://hal.univ-antilles.fr/hal-01165923</identifier>
<identifier>https://hal.univ-antilles.fr/hal-01165923/document</identifier>
<identifier>https://hal.univ-antilles.fr/hal-01165923/file/IGS_2015_submission_34.pdf</identifier>
<source>https://hal.univ-antilles.fr/hal-01165923</source>
<source>Céline Rémi; Lionel Prévost; Eric Anquetil. 17th Biennial Conference of the International Graphonomics Society, Jun 2015, Pointe-à-Pitre, Guadeloupe. 2015, Drawing, Handwriting Processing Analysis: New Advances and Challenges</source>
<language>en</language>
<subject lang=en>Sigma-lognormal model</subject>
<subject lang=en>block-learning</subject>
<subject lang=en>Handwriting Recognition</subject>
<subject>[INFO] Computer Science [cs]</subject>
<type>info:eu-repo/semantics/conferenceObject</type>
<type>Conference papers</type>
<description lang=en>In this paper, we introduce a framework for on-line learning of handwritten symbols from scratch. As such, learning suffers from missing data at the beginning of the learning process, in this paper we propose the use of Sigma-lognormal model to generate synthetic data. Our framework deals with a real-time use of the system, where the recognition of a single symbol cannot be postponed by the generation of synthetic data. We evaluate the use of our framework and Sigma-lognormal model by comparison of the recognition rate to a block-learning and learning without any synthetic data. Experimental results show that both of these contributions represent an enhancement to the on-line handwriting recognition, especially when starting from scratch.</description>
<date>2015-06-21</date>
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