(ACM, 2013) Stevens, Philip C; Blagojevic, Rachel; Plimmer, Beryl; Levent Burak Kara and Cindy Grimm
Grouping of strokes into semantically meaningful diagram elements is a difficult problem. Yet such grouping is needed if truly natural sketching is to be supported in intelligent sketch tools. Using a machine learning approach, we propose a number of new paired-stroke features for grouping and evaluate the suitability of a range of algorithms. Our evaluation shows the new features and algorithms produce promising results that are statistically better than the existing machine learning grouper.