In this paper we consider and assess the concept of using codebooks of curves to characterise a persons handwriting. This is similar to the successful methods by which handwriting has been applied to speech recognition. The handwritten signatures are scanned as binary images at 200 dpi, thinned to a single pixel width and characterised as a set of curves. Matching of signatures is achieved using a curve similarity measure. Experiments on a set of 120 handwritten signatures from six writers (20 per writer), including some forgeries, indicate the technique has potential. Whilst it does not currently perform as well as state-of-the-art signature verifiers there are numerous improvements that can be made to the technique. A number of refinements are proposed for discussion and further research.
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