Stroke level modeling of on-line hanwriting through multi-modal segmental models

Abstract : Hidden Markov Models (HMMs) have become within a few years the main technology for on line handwritten word recognition (HWR). We consider here segment models which generalize HMMs, these models aim at modeling the signal at a global level rather than at the frame level and have been shown to overcome standard HMMs in their modeling ability. We propose a new segment model which allows to automatically handle different writing styles. We compare our system on the isolated character set of the UNIPEN database to a reference system and a baseline segment model.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01572580
Contributor : Lip6 Publications <>
Submitted on : Monday, August 7, 2017 - 5:32:37 PM
Last modification on : Thursday, August 1, 2019 - 3:38:02 PM

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  • HAL Id : hal-01572580, version 1

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Thierry Artières, J.-M. Marchand, Patrick Gallinari, Bernadette Dorizzi. Stroke level modeling of on-line hanwriting through multi-modal segmental models. 7th International Workshop on Frontiers in Handwriting Recognition, Sep 2000, Amsterdam, Netherlands. pp.93-102. ⟨hal-01572580⟩

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