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The generation of synthetic handwritten data for improving on-line learning

Abstract : 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.
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Submitted on : Saturday, June 20, 2015 - 8:13:01 PM
Last modification on : Monday, March 19, 2018 - 10:38:02 PM
Long-term archiving on: : Tuesday, April 25, 2017 - 5:37:16 PM


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  • HAL Id : hal-01165923, version 1



Marta Režnáková, Lukas Tencer, Réjean Plamondon, Mohamed Cheriet. The generation of synthetic handwritten data for improving on-line learning. 17th Biennial Conference of the International Graphonomics Society, International Graphonomics Society (IGS); Université des Antilles (UA), Jun 2015, Pointe-à-Pitre, Guadeloupe. ⟨hal-01165923⟩



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