A writer identification system for on-line whiteboard data

  • Authors:
  • Andreas Schlapbach;Marcus Liwicki;Horst Bunke

  • Affiliations:
  • Institute of Computer Science and Applied Mathematics, Universität Bern, Neubrückstrasse 10, CH-3012 Bern, Switzerland;Institute of Computer Science and Applied Mathematics, Universität Bern, Neubrückstrasse 10, CH-3012 Bern, Switzerland;Institute of Computer Science and Applied Mathematics, Universität Bern, Neubrückstrasse 10, CH-3012 Bern, Switzerland

  • Venue:
  • Pattern Recognition
  • Year:
  • 2008

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Abstract

In this paper we address the task of writer identification of on-line handwriting captured from a whiteboard. Different sets of features are extracted from the recorded data and used to train a text and language independent on-line writer identification system. The system is based on Gaussian mixture models (GMMs) which provide a powerful yet simple means of representing the distribution of the features extracted from the handwritten text. The training data of all writers are used to train a universal background model (UBM) from which a client specific model is obtained by adaptation. Different sets of features are described and evaluated in this work. The system is tested using text from 200 different writers. A writer identification rate of 98.56% on the paragraph and of 88.96% on the text line level is achieved.