Topology Inference for an ANN / HMM Hybrid On-Line Handwriting Recognition System

Abstract : The paper studies a data driven design approach of HMM topology in a hybrid Neuro-Markovian system for on-line cursive handwriting recognition. Artificial neural networks (ANNs) are used as primitive models at state level and hidden Markov models (HMMs) are used at character level. Primitives are shared among all characters in the alphabet and an individual handwriting is characterized by a primitive sequence. The typical prototypes of a letter are reflected in HMM's topology. Firstly, we build a prototype analyser that creates a primitive prototype for each training example. Secondly, a number of the most typical prototypes are selected for each letter through a special clustering method. At last, letter models are built by using the selected prototypes as Markov chain's topology. The concepted system is evaluated on the wildly used UNIPEN database and the advantages are clearly approved with very encouraging results.
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Communication dans un congrès
International Conference on Neural Information systems, Nov 2002, Singapore, Singapore. IEEE, International Conference on Neural Information systems, pp.2479-2483, 〈10.1109/ICONIP.2002.1201940〉
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https://hal.archives-ouvertes.fr/hal-01561390
Contributeur : Lip6 Publications <>
Soumis le : mercredi 12 juillet 2017 - 16:44:32
Dernière modification le : vendredi 31 août 2018 - 09:25:57

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Haifeng Li, Thierry Artières, Patrick Gallinari, Bernadette Dorizzi. Topology Inference for an ANN / HMM Hybrid On-Line Handwriting Recognition System. International Conference on Neural Information systems, Nov 2002, Singapore, Singapore. IEEE, International Conference on Neural Information systems, pp.2479-2483, 〈10.1109/ICONIP.2002.1201940〉. 〈hal-01561390〉

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