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Modélisation de HMMs en contexte avec des arbres de décision pour la reconnaissance de mots manuscrits

Abstract : This paper presents an HMM-based recognizer for the off-line recognition of handwritten words. Word models are the concatenation of context-dependent character models: the trigraphs. Due to the large number of possible context-dependent models to compute, a clustering is applied on each state position, based on decision trees. Our system is shown to perform better than a baseline context independent system, and reaches an accuracy higher than 74% on the publicly available Rimes database.
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https://hal.archives-ouvertes.fr/hal-00488743
Contributor : Anne-Laure Bianne <>
Submitted on : Wednesday, June 2, 2010 - 4:27:21 PM
Last modification on : Friday, July 31, 2020 - 10:44:07 AM
Long-term archiving on: : Friday, September 17, 2010 - 12:30:09 PM

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Anne-Laure Bianne, Christopher Kermorvant, Laurence Likforman-Sulem. Modélisation de HMMs en contexte avec des arbres de décision pour la reconnaissance de mots manuscrits. Colloque International Francophone sur l'Ecrit et le Document (CIFED2010), Mar 2010, Sousse, Tunisie. ⟨hal-00488743⟩

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