Enhancing Unsupervised Signature Verification through Advanced Feature Extraction Techniques

Abstract This study delves into unsupervised handwritten signature verification, aiming to develop effective techniques without relying on labeled data. To address the challenge of limited training data, the researchers creatively employ an extensive artificially generated dataset containing signatures from over 3,000 unique individuals. The primary objective is to explore the potential of Extreme Learning Machines (ELMs) in offline signature verification, leveraging innovative unsupervised methodologies. The methodology involves several steps, including the generation of high-quality signature images, extraction of local features using a sliding window technique, and utilization of deep learning principles to train a comprehensive ELM model. The final image prediction is obtained by aggregating predictions made on local features, followed by thorough performance evaluation and assessment of practical applicability. Through meticulous methodology and rigorous evaluation, this study demonstrates a novel approach to unsupervised handwritten signature verification. By showcasing the efficacy of utilizing artificial datasets and ELMs, the research provides valuable insights for advancing signature verification systems, with potential implications for enhancing security measures across various domains.