TU Dortmund Department of Computer Science LS XII Pattern Recognition GroupPublications → Publication Details

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


Gernot A. Fink AND Thomas Pl{\"o}tz
Proc. Int. Workshop on Frontiers in Handwriting Recognition, pages 429-434, La Baule, France, 2006.

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 data-driven 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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