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Conference papers
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.
Cited literature [23 references]
https://hal.univ-antilles.fr/hal-01165923
Contributor : Philippe Simon <>
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
Contributor : Philippe Simon <>
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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IGS_2015_submission_34.pdf
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