Multi-modal segmental models for on-line handwriting recognition

Abstract : Hidden Markov models (HMMs) have become within a few years the main technology for online handwritten word recognition (HWR). We consider 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 segment model which allows us to automatically handle different writing styles. We compare our system on the isolated character set of the UNIPEN database with a reference system and a baseline segment model.
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Communication dans un congrès
15th International Conference on Pattern Recognition, Sep 2000, Barcelone, Spain. IEEE, 15th International Conference on Pattern Recognition, pp.247-250, 〈10.1109/ICPR.2000.906059〉
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https://hal.archives-ouvertes.fr/hal-01572582
Contributeur : Lip6 Publications <>
Soumis le : lundi 7 août 2017 - 17:33:46
Dernière modification le : mercredi 21 mars 2018 - 18:58:14

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Thierry Artières, J.-M. Marchand, Patrick Gallinari, Bernadette Dorizzi. Multi-modal segmental models for on-line handwriting recognition. 15th International Conference on Pattern Recognition, Sep 2000, Barcelone, Spain. IEEE, 15th International Conference on Pattern Recognition, pp.247-250, 〈10.1109/ICPR.2000.906059〉. 〈hal-01572582〉

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