HandKey: An Efficient Hand Typing Recognition using CNN for Virtual Keyboard

Avirmed Enkhbat, Timothy K. Shih, Tipajin Thaipisutikul, Noorkholis Luthfil Hakim, Wisnu Aditya

研究成果: 書貢獻/報告類型會議論文篇章同行評審

2 引文 斯高帕斯(Scopus)

摘要

This paper proposes an efficient framework that recognizes hand typing motions and gestures for making a virtual keyboard by using a single RGB camera. There are several works related to virtual keyboard in the Human-computer interaction (HCI) area. Most of them use hand pose estimation, hand shape and external equipment (depth sensor, leap motion, control glove, touch screen etc.). Whereas, our framework does not require additional equipment or prior experience from users, it works like a regular typing action in the air which is similar to typing on a real QWERTY keyboard. It uses convolutional neural networks (CNN) to classify 2 hand typing gestures (touch and non-touch). Also, we train 11 gestures which are non-touch and touching for each 10 fingers of two hands gestures. Proposed CNN model achieves a 99.2% classification accuracy for the 2 gestures case and a 91% classification accuracy for the 11 gestures case.

原文???core.languages.en_GB???
主出版物標題InCIT 2020 - 5th International Conference on Information Technology
發行者Institute of Electrical and Electronics Engineers Inc.
頁面315-319
頁數5
ISBN(電子)9781728166940
DOIs
出版狀態已出版 - 21 10月 2020
事件5th International Conference on Information Technology, InCIT 2020 - Chon Buri, Thailand
持續時間: 21 10月 202022 10月 2020

出版系列

名字InCIT 2020 - 5th International Conference on Information Technology

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???event.eventtypes.event.conference???5th International Conference on Information Technology, InCIT 2020
國家/地區Thailand
城市Chon Buri
期間21/10/2022/10/20

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