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Static signatures originate as handwritten images on documents and by definition do not contain any dynamic information. This lack of information makes static signature verification systems significantly less reliable than their dynamic counterparts. This study involves extracting dynamic information from static images, specifically the pen trajectory while the signature was created. We assume that a dynamic version of the static image is available (typically obtained during an earlier registration process). We then derive a hidden Markov model from the static image and match it to the dynamic version of the image. This match results in the estimated pen trajectory of the static image.
Index Terms- Pattern recognition, document and text processing, document analysis, handwriting analysis.

J. A. du Preez, B. Herbst and E. Nel, "Estimating the Pen Trajectories of Static Signatures Using Hidden Markov Models," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 27, no. , pp. 1733-1746, 2005.
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