Handwritten signature verification based on neural 'gas' based vector quantization

This paper propose a vector quantization (VQ) technique to solve the problem of handwritten signature verification. A neural 'gas' model is trained to establish a reference set for each registered person with handwritten signature samples. Then a test sample is compared with all the prototypes in the reference set and the system outputs the label of the writer of the word. Several different feature extraction methods are compared and good results have been obtained by the VQ technique.