Offline signature verification through probabilistic neural network

In this paper, we show the positive potential of verifying the offline handwritten signatures through discrete Radon transform (DRT), principle component analysis (PCA) and probabilistic neural network (PNN). Satisfactory results are obtained with 1.51%, 3.23%, and 13.07% equal error rate (EER) for random, casual, and skilled forgeries respectively on our independent database.

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