Handwriting Intra-Variability Across Surface Transitions: Implications for Writer Identification

Kumari Priya, Chandranath Adak, Bidyut B. Chaudhuri, Michael Blumenstein

Abstract: Handwriting exhibits intra-variability within the same writer due to friction between different writing surfaces, such as transitioning from paper to a computer tablet. This study investigates such intra-variability in handwriting characteristics across different writing surfaces and its implications for writer identification. An empirical study is conducted to assess the performance of state-of-the-art deep architectures in identifying writers amidst such intra-variation. Additionally, a transformer-based model is proposed to capture writer identification under these intra-variable circumstances. A dataset comprising 1560 handwritten English text-line images from 130 writers is created and utilized for experimentation. The results reveal insightful outcomes regarding the utilization of deep architectures and the proposed model in handling intra-variability for writer identification. This study contributes to advancing the understanding of intra-variability in handwriting and offers practical implications for forensic analysis and document authentication in the digital age.