NDLI: Speech pen: predictive handwriting based on ambient multimodal recognition

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Speech pen: predictive handwriting based on ambient multimodal recognition

Content Provider	ACM Digital Library
Author	Ogata, Jun Igarashi, Takeo Kurihara, Kazutaka Goto, Masataka
Abstract	It is tedious to handwrite long passages of text by hand. To make this process more efficient, we propose predictive handwriting that provides input predictions when the user writes by hand. A predictive handwriting system presents possible next words as a list and allows the user to select one to skip manual writing. Since it is not clear if people are willing to use prediction, we first run a user study to compare handwriting and selecting from the list. The result shows that, in Japanese, people prefer to select, especially when the expected performance gain from using selection is large. Based on these observations, we designed a multimodal input system, called speech-pen, that assists digital writing during lectures or presentations with background speech and handwriting recognition. The system recognizes speech and handwriting in the background and provides the instructor with predictions for further writing. The speech-pen system also allows the sharing of context information for predictions among the instructor and the audience; the result of the instructor's speech recognition is sent to the audience to support their own note-taking. Our preliminary study shows the effectiveness of this system and the implications for further improvements.
Starting Page	851
Ending Page	860
Page Count	10
File Format	PDF
ISBN	1595933727
DOI	10.1145/1124772.1124897
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2006-04-22
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Handwriting recognition Education Speech recognition Presentation tool Multimodal interface Context-sharing Predictive handwriting
Content Type	Text
Resource Type	Article

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