This paper describes a character generation method based on point correspondence between patterns. The number of training samples used in constructing a recognition dictionary strongly affects its recognition performance. Unfortunately, it's so time-consuming to gather large new samples that it is more useful to generate new samples from a few original ones. The character generation method proposed herein is based on the point correspondence between each sample and the template derived from all samples. The proposed method can automatically generate new samples that appear to be written naturally and extends the handwriting deformation seen in the original samples. Initial experiments show that using the samples so generated can improve the recognition performance.
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