Exploring offline signature verification techniques: a survey based on methods and future directions

One of the frequently used techniques of verifying a user as a legitimate or validated user is through signatures verification. Signature Verification could be achieved either through offline mode or by online mode. Due to limited resources required and widespread acceptance across the globe, offline signature verification is the most widely used signature verification technique. It has been carried out in several sectors including banking, finance, marketing and many other official institutes/organizations. Major challenge in the field of Offline Signature Verification (OSV) is the presence as well as the verification of forgeries in signatures. Various studies were proposed for signature verification purpose and they focused mainly on the usage of Machine Learning (ML) / Deep Learning (DL) based technologies in order to detect such forgeries in signatures, however barely few studies have described the survey that comprises of use of such recent technologies that has been used in signature verification task. Proposed work provides the categorical perspective of all the studies that were conducted in the field of OSV in recent times. Proposed study comprises of around 11 pre-existing as well as most prominent survey papers analysis summarization followed by providing novel categorical view of recent work being carried out in the field of OSV. Proposed study, along with providing most commonly used benchmark signature datasets description, primarily focuses on providing recent trends/ work (novel techniques/ ML/DL based work) that has been carried out in the field of OSV by considering 51 most prominent and relevant research papers, consolidating them in order to enlighten the work done in the field of OSV. Proposed study also provides various possible future trends/ researches that might be possible in the field of OSV.