Estimating the pen trajectories of static signatures using hidden Markov models.
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.
Nel
Emli-Mari
EM
Department of Electrical and Electronic Engineering, University of Stellenbosch, Matieland, South Africa. emnel@dsp.sun.ac.za
du Preez
Johan A
JA
Herbst
B M
BM
D000465:Algorithms; D001185:Artificial Intelligence; D001330:Electronic Data Processing / Q000379:methods*; D006236:Handwriting; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D008390:Markov Chains; D015233:Models, Statistical; D009068:Movement / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity