This paper presents a large-scale dataset on mobile text entry collected via a web-based transcription task performed by 37,370 volunteers. The average typing speed was 36.2 WPM with 2.3% uncorrected errors.
The scale of the data enables powerful statistical analyses on the correlation between typing performance and various factors, such as demographics, finger usage, and use of intelligent text entry techniques.
We report effects of age and finger usage on performance that correspond to previous studies.
We also find evidence of relationships between performance and use of intelligent text entry techniques:
auto-correct usage correlates positively with entry rates, whereas word prediction usage has a negative correlation.
To aid further work on modeling, machine learning and design improvements in mobile text entry, we make the code and dataset openly available.