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.

[1]  Hamid Reza Pourreza,et al.  Offline Handwritten Signature Identification and Verification Using Multi-Resolution Gabor Wavelet , 2011 .

[2]  Juan J. Igarza,et al.  Off-line signature recognition based on dynamic methods , 2005, SPIE Defense + Commercial Sensing.

[3]  Phalguni Gupta,et al.  Offline Signature Identification by Fusion of Multiple Classifiers using Statistical Learning Theory , 2010, ArXiv.

[4]  Mehdi Radmehr,et al.  Designing an Offline Method for Signature Recognition , 2011 .

[5]  Vandana S. Inamdar,et al.  A Preliminary Study on Various Off-line Hand Written Signature Verification Approaches , 2010 .

[6]  Ricardo Baeza-Yates,et al.  An image similarity measure based on graph matching , 2000, Proceedings Seventh International Symposium on String Processing and Information Retrieval. SPIRE 2000.

[7]  Brian C. Lovell,et al.  An Automatic Off-Line Signature Verification and Forgery Detection System , 2008 .

[8]  Phalguni Gupta,et al.  Fusion of Multiple Matchers Using SVM for Offline Signature Identification , 2009, FGIT-SecTech.

[9]  K. B. Raja,et al.  Combined Off-Line Signature Verification Using Neural Networks , 2010, ICT.

[10]  Robert Sabourin,et al.  Evaluation of a training method and of various rejection criteria for a neural network classifier used for off-line signature verification , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[11]  Robert L. Larkins,et al.  Off-line signature verification , 2009 .

[12]  Yuan Yan Tang,et al.  Off-line signature verification by the tracking of feature and stroke positions , 2003, Pattern Recognit..