Modeling writing styles for online writer identification: A hierarchical Bayesian approach

With the explosive growth of the tablet form factor and greater availability of pen-based direct input, writer identification in online environments is increasingly becoming critical for a variety of downstream applications such as intelligent and adaptive user environments, search, retrieval, indexing and digital forensics. Extant research has approached writer identification by using writing styles as a discriminative function between writers. In contrast, we model writing styles as a shared component of an individual's handwriting. We develop a theoretical framework for this conceptualization and model this using a three level hierarchical Bayesian model (Latent Dirichlet Allocation). In this text-independent, unsupervised model each writer's handwriting is modeled as a distribution over finite writing styles that are shared amongst writers. We test our model on a novel online/offline handwriting dataset IBM UB 1 which is being made available to the public. Our experiments show comparable results to current benchmarks and demonstrate the efficacy of explicitly modeling shared writing styles.