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.