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This paper presents a new method for object detection by edge grouping. This method can detect the boundaries of objects under complex background where the object contours are partly occluded or missing during contour extraction. Our method is adapted to detect the objects with not only closed boundaries but also open-boundaries. There are three contributions in this work. First, the shape of an object...
We present a new feature extraction approach to online Chinese handwriting recognition based on continuous-density hidden Markov models (CDHMM). Given an online handwriting sample, a sequence of time-ordered dominant points are extracted first, which include stroke-endings, points corresponding to local extrema of curvature, and points with a large distance to the chords formed by pairs of previously...
We study models that characterize pen trajectories of online handwritten characters in a fine manner. We propose radical based fine trajectory hidden Markov models (HMMs), which adopt radicals as basic units, and a multi-path HMM topology that emits observations with multi-space distributions (MSD) is built for each radical. Meanwhile, various stroke orders, writing styles and realness of sub-strokes...
In modeling online handwritten characters, imaginary strokes have been conveniently generated by connecting adjacent real strokes together to form a continuous trajectory. However, this approach causes confusions among characters with similar but actually different trajectories. In this paper, we propose to use multi-space probability distribution (MSD) to model imaginary strokes jointly with real...
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