Writer identification for smart meeting room systems
Marcus Liwicki, A. Schlapbach, Horst Bunke, Samy Bengio, Johnny Mariéthoz and Jonas Richiardi
In: Document Analysis Systems VII: 7th International Workshop, DAS, Lecture Notes in Computer Science, volume LNCS 3872(2006).
In this paper we present a text independent on-line writer identification system based on Gaussian Mixture Models (GMMs). This system has been developed in the context of research on Smart Meeting Rooms. The GMMs in our system are trained using two sets of features extracted from a text line. The first feature set is similar to feature sets used in signature verification systems before. It consists of information gathered for each recorded point of the handwriting, while the second feature set contains features extracted from each stroke. While both feature sets perform very favorably, the stroke-based feature set outperforms the point-based feature set in our experiments. We achieve a writer identification rate of 100% for writer sets with up to 100 writers. Increasing the number of writers to 200, the identification rate decreases to 94.75%.
|EPrint Type:||Conference or Workshop Item (Oral)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Deposited By:||Samy Bengio|
|Deposited On:||22 November 2006|