DeepWriting: Making Digital Ink Editable via Deep Generative Modeling
This addresses the challenge of preserving personalized handwriting appearance while enabling digital editing, which is incremental as it builds on existing generative modeling approaches.
The paper tackles the problem of making digital ink editable by separating content from style, enabling style transfer and word-level editing of handwritten text, and reports results from an initial user evaluation.
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.