A low-cost system for signature recognition

Automatic signature recognition or verification have many practical applications. In this paper we propose a recognition method based on handwriting acceleration, line-crossing points segmentation, macrostructures (isolated traces), chain coding and time-frequency analysis. The acceleration information is integrated twice to get a visual representation of the signature. Further processing generates coefficients and images whose characteristics can be used as a representation. These coefficients, along with dynamic information, are applied as inputs to a 3-layered neural network, to train it. The output patterns are selected to be a binary number that represents an identification index for the signer