CONF liwiki:das:2006/IDIAP Writer Identification for Smart Meeting Room Systems Liwicki, Marcus Schlapbach, Andreas Bunke, Horst Bengio, Samy Mariéthoz, Johnny Richiardi, Jonas EXTERNAL https://publications.idiap.ch/attachments/papers/2006/liwiki-das-2006.pdf PUBLIC https://publications.idiap.ch/index.php/publications/showcite/liwiki:rr05-70 Related documents Seventh IAPR Workshop on Document Analysis Systems, DAS 2006 IDIAP-RR 05-70 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\%. REPORT liwiki:rr05-70/IDIAP Writer Identification for Smart Meeting Room Systems Liwicki, Marcus Schlapbach, Andreas Bunke, Horst Bengio, Samy Mariéthoz, Johnny Richiardi, Jonas EXTERNAL https://publications.idiap.ch/attachments/reports/2005/liwiki-idiap-rr-05-70.pdf PUBLIC Idiap-RR-70-2005 2005 IDIAP Published in Seventh {IAPR} Workshop on Document Analysis Systems, {DAS}, 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\%.