Abstract
A large share of the early work on on-line handwriting recognition involved structural and syntactical methods. These approaches were soon abandoned in favor of template matching and statistical methods due to the difficulty in defining reliable rules dealing with the large variability in on-line handwritten characters. However, any method for HWR utilize the structural information implicitly and one could argue that their success depends on how well this is done. This paper presents a novel template matching method, the Frame Deformation Energy (FDE) matching, that utilizes the explicit structure of the samples to model the non-linear global variations by a set of affine transformations through a structural reparameterization. Experiments on a large data set show that for single models the FDE, despite its ad hoc implementation in this paper, outperforms conventionally used template matching schemes such as DTW and Active Shape.
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