A hierarchical framework for accent based writer identification

Writer identification is the process of determining the author of a handwritten specimen by utilizing characteristics inherent in the sample. In this work, we apply the concept of accents in handwriting to introduce a novel perspective for writer identification. Analogous to speech, accents in handwriting can be defined as distinctive writing quirks that are unique to a group of people sharing a common native script. Specifically, we postulate that a group of people with a common native script will share certain traits in their handwriting style that are exposed when they write in a different script. We propose a hierarchical framework for the writer identification task, wherein, we first identify the accent of the writer. In the next step, we perform writer identification based on the selected accent. This framework reduces the complexity of the classification task by reducing the number of classes at the prediction stage. Experiments are performed on the UNIPEN dataset and the results lend credibility to our model.