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    How to make a Quick$: Using Hierarchical Clustering toImprove the Efficiency of the Dollar Recognizer
    (The Eurographics Association, 2011) Reaver, J.; Stahovich, T. F.; Herold, J.; Tracy Hammond and Andy Nealen
    We present Quick$ (QuickBuck), an extension to the Dollar Recognizer designed to improve recognition efficiency. While the Dollar Recognizer must search all training templates to recognize an unknown symbol, Quick$ employs hierarchical clustering along with branch and bound search to do this more efficiently. Experiments have demonstrated that Quick$ is almost always faster than the Dollar Recognizer and always selects the same best-match templates.
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    ClassySeg: A Machine Learning Approach to AutomaticStroke Segmentation
    (The Eurographics Association, 2011) Herold, J.; Stahovich, T. F.; Tracy Hammond and Andy Nealen
    We present ClassySeg, a technique for segmenting hand-drawn pen strokes into lines and arcs. ClassySeg employs machine learning techniques to infer the segmentation intended by the drawer. The technique begins by identifying a set of candidate segment points, consisting of all curvature maxima. Features are computed for each candidate point based on speed, curvature, and other geometric properties. These features are adapted from numerous prior segmentation approaches, effectively combining their strengths. These features are used to train a statistical classifier to identify which candidate points are true segment points. A beam search is used to approximate the optimal subset of features to use as input to the classifier. ClassySeg is more accurate than previous techniques foruser-independent training conditions, and is as good as the current state-of-the-art algorithm for user-optimized conditions. More importantly, ClassySeg represents a movement away from prior heuristic-based approaches towards a more general and extensible approach.