UniWise:字母为Unistroke,文本预测提高手势识别

Thomas Altenburger, Anthony Subasic, B. Martin, Poika Isokoski
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引用次数: 0

摘要

目前,语言学和语言处理是文本输入领域的一个重要组成部分。预测和/或自动校正系统通常包含在具有文本输入功能的移动设备中。然而,在基于手势的文本输入中,很少有人知道这样的系统。我们提出了UniWise,一个结合了Unistrokes(一种手势字母)和LetterWise(一种消歧系统)的系统。我们的目的是研究手势识别结果和预测系统结果的融合。我们的系统用几个融合功能进行了测试。最优函数的理论识别错误率提高了17.23%。进行了一个实验来衡量对用户性能的影响。结果表明,最后三次的识别错误率提高了17.26%,正确字符在识别结果列表中的位置提高了1.04%。根据用户反馈,LetterWise组件对用户是透明的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
UniWise: LetterWise pour Unistroke, la prédiction de texte pour améliorer la reconnaissance de geste
Nowadays, linguistics and language processing represent an important part of the text entry domain. Prediction and/or automatic correction systems are often included in mobile devices with text entry capabilities. However, in gesture based text entry, few such systems are known. We propose UniWise, a system which combines Unistrokes, a gesture alphabet, and LetterWise, a disambiguation system. Our purpose was to study the fusion of the results from the gesture recognizer and results of the prediction system. Our system was tested with several fusion functions. The best function showed a theorical improvement of the recognition error rate of 17,23%. An experiment was conducted to measure the impact on user performance. The result was an improvement of 17,26% in the recognition error rate in the three last sessions and an improvement of 1,04 for the position of the correct character in the recognition result list. According to user feedback, the LetterWise component was transparent to the users.
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