Multilingual Touchscreen Keyboard Design and Optimization

languages, and minimize it by the Metropolis algorithm. Given m different languages,
the mean time t of tapping a character across these m languages is calculated as:
t
t
m
i � � (7)
i�1
m
t i represents the mean time of inputting a character in language i , which is estimated
by the Fitts-digraph model (Equation 5). t is then regarded as the objective function and
minimized by the Metropolis algorithm. The optimization process is similar to previous
work (Zhai, Hunter, et al., 2002), except that the “virtual energy” ( t new and t old ) is
estimated by Equation 7.
The average in Equation 7 can be possibly weighted according to each language’s
speaker population or some other criteria. Although any weighting scheme can be
controversial, the same methodology and procedure presented in this paper should still
apply. We stay with Equation 7 as the objective function in the present work.
3.2 Optimization for English and French
We start with optimizing a keyboard for both English and French. These two
languages are commonly used in the world: it is estimated that over 470 million people
speak English and more than 250 million people speak French. The keyboard optimized
for these two languages could benefit many English-French speakers.
As shown in Figure 3 and 4., although English and French vary in many aspects, their
letter and digraph frequency distributions are strongly correlated, with correlation
coefficients 0.88 (letter) and 0.75 (digraph) respectively. These high correlations are
encouraging for our optimization objective. Note that the English corpus is obtained from
ANC, and corpora of other languages are from http://corpus.leeds.ac.uk/list.html .
16%
14%
12%
10%
8%
6%
4%
2%
0%
Figure 3. Letter Frequency of English and French
a b c d e f g h i j k l m n o p q r s t u v w x y z
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English French