  Digital ink promises to combine the flexibility and aesthetics of handwriting
and the ability to process, search and edit digital text. Character recognition
converts handwritten text into a digital representation, albeit at the cost of
losing personalized appearance due to the technical difficulties of separating
the interwoven components of content and style. In this paper, we propose a
novel generative neural network architecture that is capable of disentangling
style from content and thus making digital ink editable. Our model can
synthesize arbitrary text, while giving users control over the visual
appearance (style). For example, allowing for style transfer without changing
the content, editing of digital ink at the word level and other application
scenarios such as spell-checking and correction of handwritten text. We
furthermore contribute a new dataset of handwritten text with fine-grained
annotations at the character level and report results from an initial user
evaluation.
