Unsupervised Estimation of Writing Style Models for Improved Unconstrained Off-line Handwriting Recognition

Abstract : The performance of writer-independent unconstrained handwriting recognition is severely affected by variations in writing style. In a segmentation-free approach based on Hidden-Markov models we, therefore, use multiple recognition models specialized to specific writing styles in order to improve recognition performance. As the explicit definition of writing styles is not obvious we propose an unsupervised clustering procedure that estimates Gaussian mixture models for writing styles in a completely datadriven manner and thus implicitly establishes classes of writing styles. On a challenging writer-independent unconstrained handwriting recognition task our two stage recognition approach – first performing a writing style classification and then using a style-specific writing model for decoding – achieves superior performance compared to a single style-independent baseline system.
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Communication dans un congrès
Guy Lorette. Tenth International Workshop on Frontiers in Handwriting Recognition, Oct 2006, La Baule (France), Suvisoft, 2006


https://hal.inria.fr/inria-00103797
Contributeur : Anne Jaigu <>
Soumis le : mardi 21 novembre 2006 - 09:11:14
Dernière modification le : mardi 21 novembre 2006 - 10:18:15

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  • HAL Id : inria-00103797, version 2

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Gernot A. Fink, Thomas Plötz. Unsupervised Estimation of Writing Style Models for Improved Unconstrained Off-line Handwriting Recognition. Guy Lorette. Tenth International Workshop on Frontiers in Handwriting Recognition, Oct 2006, La Baule (France), Suvisoft, 2006. <inria-00103797v2>

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