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As the rise of pen-enabled interfaces is accompanied with an increased number of techniques for recognition of pen-based input, recent trends in symbol recognition show an escalation in systems complexity (number of features, classifiers combination) or the over-specialization of systems to specific datasets or applications. In spite of the importance of(More)
In this paper, we introduce the Intuidoc-Loustic Gestures DataBase (ILGDB), a new publicly available database of realistic pen-based gestures for evaluation of recognition systems in pen-enabled interfaces. IL-GDB was collected in a real world context and in an immersive environment. As it contains a large number of unconstrained user-defined gestures,(More)
In this paper, we present a new type of Marking menus. Continuous Marking Menus are specifically dedicated to pen-based interfaces, and designed to define a set of cursive, realistic handwritten gestures. In menu mode, they offer a continuous visual feedback and fluent exploration of menu hierarchy, inviting the user to execute cursive gestures for invoking(More)
We present here the Continuous Marking Menus, which help users learning a set of handwritten commands on a pen-based interface. The aim of this paper is to experimentally attest the interest of this new type of menu by evaluating its ability to help the learning of a set of gestures. We describe an experimental comparison on the task of learning a set of(More)
The hierarchical nature of Chinese characters has inspired radical-based recognition, but radical segmentation from characters remains a challenge. We previously proposed a radical-based approach for on-line handwritten Chinese character recognition, which incorporates character structure knowledge into integrated radical segmentation and recognition, and(More)