Assessment of Offline Digital Signature Recognition Classification Techniques

The digital signature verification has become an interesting domain, which is widely needed. The usage of online and offline digital signatures has been spreaded worldwide due to the increase of use of bank transactions and user authentication and other similar activities. This requires the creation and the diversification of new online and offline signature verification methods. The signature verification methods contain both online (or dynamic) and offline (or static) signature verification methods. In this paper, an offline digital signature verification technique is proposed, that depends on extracting several features from the signatures to be used during simulation. Some signatures were used for training and others were used for testing only. Different methods such as, vectors manipulation, ensemble classification using boosted trees, and bagged trees, were used in this paper during simulation to obtain results.