Advances in Offline Handwritten Signature Recognition Research: A Review

A person's handwritten signature, one of the methods frequently used to confirm their identity, is used exclusively to confirm the biometric identification of different financial, legal, banking, insurance, and other business documents. Signature recognition techniques are used to identify which user someone's signature is affiliated with. In recent years, many researchers have worked on applying new methods to this work, and deep learning methods have become quite prevalent among them. In order to provide more researchers with a better comprehension of how offline handwritten signature recognition work has evolved, the existing approaches, different architectures, challenging issues, and trends within the last 15 years, this paper follows a protocol to organize this work, collects information primarily from studies published in four major databases, applies inclusion and exclusion criteria, reviews offline handwritten signature recognition methods, including issues such as feature extraction and classification, and attempts to summarize the challenges and opportunities in the field. This paper emphasizes the popularity of research directions in this research area in deep learning. In contrast to other surveys in the field, this paper is not limited to a particular phase of work but provides a detailed account of each stage with the expectation that this will help future researchers.