arXiv:2606.25247v1 Announce Type: cross Abstract: Neural swipe decoders are typically tied to the keyboard they were trained on, requiring a new corpus and training run for each layout. In this report, we document our approach toward training models that can function on any contiguous mobile keyboard layout. At each point along the swipe, our encoder predicts whether the user is indicating a character and where on the keyboard that character lies. The keyboard layout is supplied at inference time and used to map the spatial and temporal prediction to a logit at each key, rather than being lear
The continuous drive for more flexible and efficient human-computer interaction, especially in mobile contexts, leads to research in adaptable input methods.
This development could significantly enhance the user experience and accessibility of mobile typing by decoupling swipe decoding from specific keyboard layouts, making HMI more adaptable.
Traditional neural swipe decoders required retraining for each keyboard layout; this approach allows a single model to adapt to any contiguous mobile keyboard layout at inference time.
- · Mobile OS developers
- · Keyboard app developers
- · Users of mobile devices
- · AI/ML researchers
- · Companies with proprietary, layout-specific swipe decoding IP
Improved, more fluid mobile typing experience across diverse layouts and languages.
Potential for new, dynamic keyboard layouts that adapt to user preferences or context without requiring specific model retraining.
Enhanced accessibility features for users with different motor skills or visual impairments by allowing highly customizable input surfaces.
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Read at arXiv cs.LG