  Handwriting is a natural and versatile method for human-computer interaction,
especially on small mobile devices such as smart phones. However, as
handwriting varies significantly from person to person, it is difficult to
design handwriting recognizers that perform well for all users. A natural
solution is to use machine learning to adapt the recognizer to the user. One
complicating factor is that, as the computer adapts to the user, the user also
adapts to the computer and probably changes their handwriting. This paper
investigates the dynamics of co-adaptation, a process in which both the
computer and the user are adapting their behaviors in order to improve the
speed and accuracy of the communication through handwriting. We devised an
information-theoretic framework for quantifying the efficiency of a handwriting
system where the system includes both the user and the computer. Using this
framework, we analyzed data collected from an adaptive handwriting recognition
system and characterized the impact of machine adaptation and of human
adaptation. We found that both machine adaptation and human adaptation have
significant impact on the input rate and must be considered together in order
to improve the efficiency of the system as a whole.
