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2008

A writer identification system for on-line whiteboard data

14 years 1 days ago
A writer identification system for on-line whiteboard data
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. Key words: writer identification, on-line handwriting, Gaussian mixture models
Andreas Schlapbach, Marcus Liwicki, Horst Bunke
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2008
Where PR
Authors Andreas Schlapbach, Marcus Liwicki, Horst Bunke
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