Gesture-typing is an efficient, easy-to-learn, and error tolerant technique for entering text on software keyboards. Our goal is to "recycle" users' otherwise-unused gesture variation to create rich output under the users' control, without sacrificing accuracy.
We introduce Expressive Keyboards, an approach that takes advantage of rich variation in gesture-typed input to produce expressive output. Our goal is to increase information transfer in textual communication with an instrument that enables users to express themselves through personal style and through intentional control. This approach adds a layer of gesture analysis, separate from the recognition process, that quantifies the differences between the gesture template and the gesture actually drawn on the keyboard. These features can then be mapped to output properties and rendered as rich output, such as dynamic fonts or parametric emoticon.