Co-adaptation in a Handwriting Recognition System

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 solu-tion 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 prob-ably changes their handwriting. This paper investigates the dynamics of co-adaptation, a process in which both the com-puter and the user are adapting their behaviors in order to im-prove 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 sig-nificant impact on the input rate and must be considered to-gether in order to improve the efficiency of the system as a whole. Author Keywords Co-adaptation; handwriting recognition; communication channel