Offline Signature Recognition System Using Radon Transform

A novel approach for off-line signature recognition system is presented in this work, which is based on local radon features. The proposed system functions in three stages. Pre-processing stage, which consists of three steps: gray scale conversion, binarisation and fitting boundary box in order to make signatures ready for feature extraction, Feature extraction stage, where totally 16 radon transform based projection features are extracted which are used to distinguish the different signatures. Finally in Neural Network stage, an efficient Back Propagation Neural Network (BPNN) is designed and trained with 16 extracted features. The trained Neural Network is further used for signature recognition after the process of feature extraction. The average recognition accuracy obtained using this model ranges from 97%-87% with the training set of 10-40 persons.