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

• Type: Journal
• Year: 2008
• Where: PR
• Authors: Andreas Schlapbach, Marcus Liwicki, Horst Bunke
• Keywords: Gaussian Mixture Models, On-line Writer Identification, PR 2008, Writer Identification