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ID 59962
フルテキストURL
fulltext.pdf 3.18 MB
著者
Huda, Samsul Department of Electrical and Communication Engineering, Okayama University
Funabiki, Nobuo Department of Electrical and Communication Engineering, Okayama University Kaken ID publons researchmap
Kuribayashi, Minoru Department of Electrical and Communication Engineering, Okayama University ORCID Kaken ID publons researchmap
Kao, Wen-Chung Department of Electrical Engineering, National Taiwan Normal University
抄録
Purpose
For several decades, calligraphy has been popular among people in Japan, China, and even in the world. Traditionally, a teacher teaches how to write letters on a paper with a brush, and a student will imitate them by referring to the model letters. However, if a teacher is not available, this method will not be applicable either. This study aims to propose a calligraphy learning assistant system (CLAS) using projection mapping, which allows a student to learn calligraphy by him/herself.
Design/methodology/approach
By following the letter writing video of a teacher that is directly projected on the paper, a student is able to learn the stroke order and writing speed in addition to the letter shape. Moreover, the letter portion practice function is incorporated in CLAS to allow a learner to repeat practicing hard portions of each letter.
Findings
For evaluations, the authors implemented CLAS using Raspberry Pi and open-source software and asked students to use it. The results confirmed that CLAS is effective in improving calligraphy skills of novice students.
Originality/value
With CLAS, a student can practice calligraphy using a conventional brush, ink and paper at a desk while looking at the model letter writing of a teacher projected on the paper using projection mapping.
キーワード
Raspberry Pi
Calligraphy
Learning assistant system
Projection mapping
発行日
2019-10-23
出版物タイトル
International Journal of Web Information Systems
16巻
2号
出版者
Emerald
開始ページ
137
終了ページ
149
ISSN
1744-0084
資料タイプ
学術雑誌論文
言語
English
OAI-PMH Set
岡山大学
著作権者
© Emerald Publishing Limited
論文のバージョン
author
DOI
Web of Science KeyUT
関連URL
isVersionOf https://doi.org/10.1108/IJWIS-07-2019-0032