DuSK: Faster Indirect Text Entry Supporting Out-Of-Vocabulary Words for Touchpads
Damien Masson, Zhe Liu, Charles Xu
TL;DR
DuSK introduces a deterministic, two-thumb, stroke-based keyboarding technique for eyes-free text entry on touchpads, designed to support out-of-vocabulary words without relying on language models. Through iterative design and two controlled studies, DuSK demonstrates speeds up to about $13$ WPM for novices and approaches sighted tap-typing performance for experts, while outperforming cursor-based approaches that dominate SmartTV interfaces. The method combines length- and angle-based strokes, a transfer function between touchpad and display, and Bayesian word correction/completion to balance OOV input with dictionary-supported predictions. This work enables reliable OOV word input in VR and SmartTV contexts where the input device is decoupled from the display, offering a practical alternative to lexicon-dependent methods and a foundation for future device-agnostic eyes-free input systems.
Abstract
Given the ubiquity of SmartTVs and head-mounted-display-based virtual environments, recent research has explored techniques to support eyes-free text entry using touchscreen devices. However, proposed techniques, leveraging lexicons, limit the user's ability to enter out-of-vocabulary words. In this paper, we investigate how to enter text while relying on unambiguous input to support out-of-vocabulary words. Through an iterative design approach, and after a careful investigation of actions that can be accurately and rapidly performed eyes-free, we devise DuSK, a {Du}al-handed, {S}troke-based, 1{K}eyboarding technique. In a controlled experiment, we show initial speeds of 10 WPM steadily increasing to 13~WPM with training. DuSK outperforms the common cursor-based text entry technique widely deployed in commercial SmartTVs (8 WPM) and is comparable to other eyes-free lexicon-based techniques, but with the added benefit of supporting out-of-vocabulary word input.