  Gesture typing is a method of text entry that is ergonomically well-suited to
the form factor of touchscreen devices and allows for much faster input than
tapping each letter individually. The QWERTY keyboard was, however, not
designed with gesture input in mind and its particular layout results in a high
frequency of gesture recognition errors. In this paper, we describe a new
approach to quantifying the frequency of gesture input recognition errors
through the use of modeling and simulating realistically imperfect user input.
We introduce new methodologies for modeling randomized gesture inputs,
efficiently reconstructing words from gestures on arbitrary keyboard layouts,
and using these in conjunction with a frequency weighted lexicon to perform
Monte Carlo evaluations of keyboard error rates or any other arbitrary metric.
An open source framework, Dodona, is also provided that allows for these
techniques to be easily employed and customized in the evaluation of a wide
spectrum of possible keyboards and input methods. Finally, we perform an
optimization procedure over permutations of the QWERTY keyboard to demonstrate
the effectiveness of this approach and describe ways that future analyses can
build upon these results.
