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

Gernot A. Fink 1 Thomas Plötz 2
1 Robotics Research Institute - Intelligent Systems Group
2 Faculty of Technology - Applied Computer Science Group
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.
Keywords : Unconstrained handwriting segmentation-fre recognition writing-style model
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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
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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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