Data Driven Design of an ANN/HMM System for On-Line Unconstrained Handwritten Character Recognition

Abstract : This paper is dedicated to a data driven design method for a hybrid ANN/HMM based handwriting recognition system. On one hand, a data driven designed neural modelling of handwriting primitives is proposed. ANNs are firstly used as state models in a HMM primitive divider that associates each signal frame with an ANN by minimizing the accumulated prediction error. Then, the neural modelling is realized by training each network on its own frame set. Organizing these two steps in an EM algorithm, precise primitive models are obtained. On the other hand, a data driven systematic method is proposed for the HMM topology inference task. All possible prototypes of a pattern class are firstly merged into several clusters by a tabu search aided clustering algorithm. Then a multiple parallel-path HMM is constructed for the pattern class. Experiments prove an 8% recognition improvement with a saving of 50% of system resources, compared to an intuitively designed referential ANN/HMM system.
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Communication dans un congrès
4th International Conference on Multimodal interfaces, Oct 2002, Pittsburgh, PA, United States. IEEE, 4th International Conference on Multimodal interfaces, pp.149-154, 〈10.1109/ICMI.2002.1166984〉
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https://hal.archives-ouvertes.fr/hal-01561398
Contributeur : Lip6 Publications <>
Soumis le : mercredi 12 juillet 2017 - 16:52:17
Dernière modification le : vendredi 31 août 2018 - 09:25:57

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Haifeng Li, Thierry Artières, Patrick Gallinari. Data Driven Design of an ANN/HMM System for On-Line Unconstrained Handwritten Character Recognition. 4th International Conference on Multimodal interfaces, Oct 2002, Pittsburgh, PA, United States. IEEE, 4th International Conference on Multimodal interfaces, pp.149-154, 〈10.1109/ICMI.2002.1166984〉. 〈hal-01561398〉

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