A REVIEW PAPER ON OFFLINE SIGNATURE RECOGNITION SYSTEM USING RADON TRANSFORM WITH GENETIC ALGORITHM

Hand handwritten signatures are generally acknowledged as a method for archive confirmation, approval and individual check. For legitimateness most archives like bank checks, travel visas and scholastic declarations need to have approved handwritten signatures. In modern world where extortion is widespread, there is the requirement for a programmed HSV (Handwritten sign ature verification) framework to supplement visual confirmation. Automated signature verification is as vital as other programmed distinguishing proof frameworks; however they vary from different frameworks that depend on ownership of keys and so forth or learning of particular individual data like passwords. So, in this proposed work signature recognition will be done using Radon Transform, GA and Neural network. The proposed work has divided into 2 panels: training and testing panel. In training panel tra ining will be done using neural network. Then in testing panel testing is done using Radon Transform and GA. In the end performance has been evaluated using FAR, FRR and accuracy in MATLAB 7.10 simulation. From the results it has been calculated that neura l network works much better as compared to existing technique.