Writer Identification from Nordic Historical Manuscripts using Transformer Networks

Handwriting has been used as a form of authentication for the last 1000 years. Forensic analysis of handwriting using computers has been in practice since the 1970s. With the evolution of deep-learning techniques over the last decade, such automated forensic analysis of handwritten text has become dependent on deep-learning techniques. In this paper, we investigate the prowess of transformer networks in the context of identifying the writer of a handwritten sample. We here propose a deep feature embedding-based transformer network, WiT, for writer identification. Experiments were conducted on a historical Nordic manuscript dataset comprising 9253 handwritten samples scribbled by 50 writers for the very first time, and encouraging results were obtained. Rigorous experiments were also conducted to check the noise / damage resiliency of WiT, and the outcomes were quite promising.