Offline Signature Verification and Forgery Detection Approach

Signature verification and forgery detection is a challenging field with a lot of critical issues. Signatures forgery drives cooperates and business organizations to huge financial loss and also affects their security reputation. Highly accurate automatic systems are needed in order to prevent this kind of crimes. This paper introduce an automatic off-line system for signature verification and forgery detection. Different features were extracted and their effect on system recognition ability was reported. The computed features include run length distributions, slant distribution, entropy, Histogram of Gradients features (HoG) and Geometric features. Finally, different machine learning techniques were applied on the computed features: bagging tree, random forest and Support Vector Machine (SVM). it was noticed that SVM outperforms the other classifiers when applied on HoG features. The system was applied on Persian Offline Signature Data-set (UTSig) database and achieved satisfactory results in differentiating between genuine and forged signature.