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dc.contributor.authorStevens, Philip Cen_US
dc.contributor.authorBlagojevic, Rachelen_US
dc.contributor.authorPlimmer, Berylen_US
dc.contributor.editorLevent Burak Kara and Cindy Grimmen_US
dc.date.accessioned2016-02-18T11:38:47Z
dc.date.available2016-02-18T11:38:47Z
dc.date.issued2013en_US
dc.identifier.isbn978-1-4503-2205-8en_US
dc.identifier.issn1812-3503en_US
dc.identifier.urihttp://dx.doi.org/10.1145/2487381.2487383en_US
dc.description.abstractGrouping 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.en_US
dc.publisherACMen_US
dc.subjectI.7.5 [Document and Text Processing]en_US
dc.subjectDocument Captureen_US
dc.subjectGraphics recognition and interpretationen_US
dc.subjectI.2.5 [Artificial Intelligence]en_US
dc.subjectProgramming Languages and Softwareen_US
dc.subjectExpert system tools and techniques. Keywordsen_US
dc.subjectDigital Ink recognitionen_US
dc.subjectgrouping strokesen_US
dc.titleSupervised Machine Learning for Grouping Sketch Diagram Strokesen_US
dc.description.seriesinformationEurographics Workshop on Sketch-Based Interfaces and Modelingen_US
dc.description.sectionheadersSegmenting Sketchesen_US
dc.identifier.doi10.1145/2487381.2487383en_US
dc.identifier.pages43-52en_US


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