Efficient piecewise-linear function approximation using the uniform metric: (preliminary version)
SCG '94 Proceedings of the tenth annual symposium on Computational geometry
A Fast Statistical Mixture Algorithm for On-Line Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Saliency-Based Multiscale Method for On-Line Cursive Handwriting Shape Description
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
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The coarse to fine search methodology is frequently applied to a wide variety of problems in computer vision. In this paper it is shown that this strategy can be used to enhance the recognition of on-line handwritten characters. Some explicit knowledge about the structure of a handwritten character can be obtained through a structural parameterization. The Frame Deformation Energy matching (FDE) method is a method optimized to include such knowledge in the discrimination process. This paper presents a novel parameterization strategy, the Djikstra Curve Maximization (DCM) method, for the segments of the structural frame. Since this method distributes points unevenly on each segment, point-to-point matching strategies are not suitable. A new distance measure for these segment-to-segment comparisons have been developed. Experiments have been conducted with various settings for the new FDE on a large data set both with a single model matching scheme and with a kNN type template matching scheme. The results reveal that the FDE even in an ad hoc implementation is a robust matching method with recognition results well comparing to the existing state-of-the-art methods.