Publication: Co-adaptation in a Handwriting Recognition System
Issued Date
2018-09-06
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2-s2.0-85057763317
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Mahidol University
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SCOPUS
Bibliographic Citation
Proceeding of 2018 15th International Joint Conference on Computer Science and Software Engineering, JCSSE 2018. (2018)
Suggested Citation
Sunsern Cheamanunkul, Yoav Freund Co-adaptation in a Handwriting Recognition System. Proceeding of 2018 15th International Joint Conference on Computer Science and Software Engineering, JCSSE 2018. (2018). doi:10.1109/JCSSE.2018.8457173 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/45577
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Title
Co-adaptation in a Handwriting Recognition System
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Abstract
© 2018 IEEE. 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 coadaptation, 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.