VelociTap: Investigating Fast Mobile Text Entry using Sentence-based Decoding of Touchscreen Keyboard Input

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VelociTap: Investigating Fast Mobile Text Entry using Sentence-based Decoding of Touchscreen Keyboard Input

Keith Vertanen, Haythem Memmi, Justin Emge, Shyam Reyal, Per Ola Kristensson

CHI '15: Proceedings of the ACM Conference on Human Factors in Computing Systems, 2015.

We present VelociTap: a state-of-the-art touchscreen keyboard decoder that supports a sentence-based text entry approach. VelociTap enables users to seamlessly choose from three word-delimiter actions: pushing a space key, swiping to the right, or simply omitting the space key and letting the decoder infer spaces automatically. We demonstrate that VelociTap has a significantly lower error rate than Googles keyboard while retaining the same entry rate. We show that intermediate visual feedback does not significantly affect entry or error rates and we find that using the space key results in the most accurate results. We also demonstrate that enabling flexible word-delimiter options does not incur an error rate penalty. Finally, we investigate how small we can make the keyboard when using VelociTap. We show that novice users can reach a mean entry rate of 41 wpm on a 40mm wide smartwatch-sized keyboard at a 3% character error rate.

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