An Efficient Graph-Based Symbol Recognizer

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dc.contributor.authorLee, WeeSanen_US
dc.contributor.authorKara, Levent Buraken_US
dc.contributor.authorStahovich, Thomas F.en_US
dc.contributor.editorThomas Stahovich and Mario Costa Sousaen_US
dc.date.accessioned2014-01-27T19:17:05Z
dc.date.available2014-01-27T19:17:05Z
dc.date.issued2006en_US
dc.description.abstractWe describe a trainable symbol recognizer for pen-based user interfaces. Symbols are represented internally as attributed relational graphs that describe both the geometry and topology of the symbols. Symbol recognition reduces to the task of finding the definition symbol whose attributed relational graph best matches that of the unknown symbol. One challenge addressed in the current work is how to perform this graph matching in an effi- cient fashion so as to achieve interactive performance. We present four approximate graph matching techniques: Stochastic Matching, which is based on stochastic search; Error-driven Matching, which uses local matching errors to drive the solution to an optimal match; Greedy Matching, which uses greedy search; and Sort Matching, which relies on geometric information to accelerate the matching. Finally, we present promising results of initial user studies, and discuss the tradeoffs between the various matching techniques.en_US
dc.description.seriesinformationEurographics Workshop on Sketch-Based Interfaces and Modelingen_US
dc.identifier.isbn3-905673-39-8en_US
dc.identifier.issn1812-3503en_US
dc.identifier.urihttps://doi.org/10.2312/SBM/SBM06/011-018en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): I.5.2 [Pattern Recognition]: Classifier Design and Evaluationen_US
dc.titleAn Efficient Graph-Based Symbol Recognizeren_US
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SBM06: Sketch Based Interfaces and Modeling 2006