An extensible digital ink segmentation and classification framework for natural notetaking

Authors:
Adriana Ispas;Beat Signer;Moira C. Norrie
Affiliations:
ETH Zurich, Zurich, Switzerland;Vrije Universiteit Brussel, Brussels, Belgium;ETH Zurich, Zurich, Switzerland
Venue:
Proceedings of the 3rd ACM SIGCHI symposium on Engineering interactive computing systems
Year:
2011

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Abstract

With the emergence of digital pen and paper technologies, we have witnessed an increasing number of enhanced paper-digital notetaking solutions. However, the natural notetaking process includes a variety of individual work practices that complicate the automatic processing of paper notes and require user intervention for the classification of digital ink data. We present an extensible digital ink processing framework that simplifies the classification of digital ink data in natural notetaking applications. Our solution deals with the manual as well as automatic ink data segmentation and classification based on Delaunay triangulation and a strongest link algorithm. We further highlight how our solution can be extended with new digital ink classifiers and describe a paper-digital reminder application that has been realised based on the presented digital ink processing framework.