Generative Ink: Data-Driven Computational Models for Digital Ink

Abstract

Digital ink promises to combine the flexibility of pen and paper interaction and the versatility of digital devices. Computational models of digital ink often focus on recognition of the content by following discriminative techniques such as classification, albeit at the cost of ignoring or losing personalized style. In this chapter, we propose augmenting the digital ink framework via generative modeling to achieve a holistic understanding of the ink content. Our focus particularly lies in developing novel generative models to gain fine-grained control by preserving user style. To this end, we model the inking process and learn to create ink samples similar to users. We first present how digital handwriting can be disentangled into style and content to implement editable digital ink, enabling content synthesis and editing. Second, we address a more complex setup of free-form sketching and propose a novel approach for modeling stroke-based data efficiently. Generative ink promises novel functionalities, leading to compelling applications to enhance the inking experience for users in an interactive and collaborative manner.