DeepWriting: Making Digital Ink Editable via Deep Generative Modeling
Published in
International Conference on Human Factors in Co...(2018)External Links:
TL
TL;DR
This paper proposes a novel generative neural network architecture that is capable of disentangling style from content and thus making digital ink editable, and can synthesize arbitrary text, while giving users control over the visual appearance.
Abstract
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.
References65 items
1
"The pen is mightier than the keyboard: Advantages of longhand over laptop note taking": Corrigendum.
2
DeLiGAN: Generative Adversarial Networks for Diverse and Limited Data
3
A sigma-lognormal model-based approach to generating large synthetic online handwriting sample databases
4
As We May Ink?: Learning from Everyday Analog Pen Use to Improve Digital Ink Experiences
5