Co-adaptation in a Handwriting Recognition System

TL;DR

This paper explores the co-adaptation process in handwriting recognition systems, analyzing how both user and machine adaptations influence system efficiency and communication speed.

Contribution

It introduces an information-theoretic framework to quantify the combined effects of human and machine adaptation in handwriting recognition.

Findings

Both machine and human adaptations significantly improve input rate.

Considering both adaptations together enhances overall system efficiency.

Analysis of collected data reveals the dynamic interplay between user and system adaptations.

Abstract

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.