Handwritten Signature Recognition,Verification and Dynamic Updation using Neural Network

Abstract: The hand written signature is regarded as the primary means of identifying the signer of a written document based on the implicit assumption that a person�s normal signature changes slowly and is very difficult to erase, alter or forge without detection. The handwritten signature is one of the ways to authorize transactions and authenticate the human identity compared with other electronic identification methods such as fingerprints scanning, face recognition and retinal vascular pattern screening. It is easier for people to migrate from using the popular pen-and-paper signature to one where the handwritten signature is captured and verified electronically. The signature of a person is an important biometric attribute of a human being and is used for authorization purpose. Various approaches are possible for signature recognition with a lot of scope of research. Here, we deal with an off-line signature recognition technique. Signatures are composed of special characters and flourishes and therefore most of the time they can be unreadable. Also intrapersonal variations and interpersonal differences make it necessary to analyze them as complete images and not as letters and words put together. Signature recognition is the process of verifying the writer�s identity by checking the signature against samples kept in the database. The result of this process is usually between 0 and 1 which represents a fit ratio (1 for match and 0 for mismatch). Signature recognition is used most often to describe the ability of a computer to translate human writing into text. This may take place in one of two ways either by scanning of written text (off-line method) or by writing directly on to a peripheral input device.

Keywords: OCR (Optical Character Recognition), FRR (False Rejection Rate).