Gesture-based auto-completion of handwriten text

Abstract

Auto-completion is a major feature in all keyboard based mobile devices. However, not a lot of work has been done to extend this feature into handwritten text based input. Generally, auto-complete options are ranked on the basis of previous context and word probabilities. However, this leads to missing out on the user intended word simply because there are more frequently used words with the same prefix, in the same context. In this paper, we propose a gesture-based solution to recognize and complete partially-written words, where the user indicates to the system the missing text length by a stroke gesture, thus ruling out a huge subset of possibilities. We also propose a length dependent hybrid score ranking system, to improve the prediction accuracy and speed. Our results show that using a gesture stroke length based searching method not only reduced the processing time by 27% but also showed an accuracy increase of 10%, when the top 3 candidates are considered.