Off-lineWriter Identification Using Gaussian Mixture Models

Authors:
Andreas Schlapbach;Horst Bunke
Affiliations:
University of Bern, Switzerland;University of Bern, Switzerland
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Year:
2006

Citing 0
Cited 4

A writer identification system for on-line whiteboard data

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Text independent writer recognition using redundant writing patterns with contour-based orientation and curvature features

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Offline text-independent writer identification using codebook and efficient code extraction methods

Image and Vision Computing
Text-independent writer recognition using multi-script handwritten texts

Pattern Recognition Letters

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Abstract

Writer identification is the task of determining the author of a sample handwriting from a set of writers. In this paper, we propose Gaussian Mixture Models (GMMs) to address the task of off-line, text independent writer identification of text lines. The resulting system is compared to a system that uses a Hidden Markov Model (HMM) based approach. While the GMM based system is conceptually much simpler and faster to train than the HMM based system, it achieves a significantly higher writer identification rate of 98.46% on a data set of 4,103 text lines coming from 100 writers.