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深層学習を用いたチェックマーク式アンケートの自動認識に関する一検討
https://kindai.repo.nii.ac.jp/records/21035
https://kindai.repo.nii.ac.jp/records/210357d9b1328-f007-469c-a626-25be916dfda7
名前 / ファイル | ライセンス | アクション |
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AA12315376-20200315-0067.pdf (483.4 kB)
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Item type | ☆紀要論文 / Departmental Bulletin Paper(1) | |||||
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公開日 | 2020-07-13 | |||||
タイトル | ||||||
タイトル | 深層学習を用いたチェックマーク式アンケートの自動認識に関する一検討 | |||||
タイトル | ||||||
言語 | en | |||||
タイトル | A Study on Automatic Recognition of Checkmark Questionnaire Using Deep Learning | |||||
著者 |
岩佐, 英彦
× 岩佐, 英彦× 宮崎, 勢三 |
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言語 | ||||||
言語 | jpn | |||||
キーワード | ||||||
主題 | Deep learning, Checkmark recognition, Automatic survey aggregation | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
資源タイプ | departmental bulletin paper | |||||
著者(英) | ||||||
en | ||||||
IWASA, Hidehiko | ||||||
著者(英) | ||||||
en | ||||||
MIYAZAKI, Seizo | ||||||
著者 所属 | ||||||
近畿大学工業高等専門学校 総合システム工学科 制御情報コース | ||||||
著者 所属 | ||||||
独立行政法人 国立印刷局 | ||||||
著者所属(翻訳) | ||||||
Kindai University Technical College | ||||||
著者所属(翻訳) | ||||||
National Printuing Bureau | ||||||
版 | ||||||
出版タイプ | NA | |||||
出版タイプResource | http://purl.org/coar/version/c_be7fb7dd8ff6fe43 | |||||
出版者 名前 | ||||||
出版者 | 近畿大学工業高等専門学校 | |||||
書誌情報 |
近畿大学工業高等専門学校研究紀要 en : Research reports Kindai University Technical College 号 13, p. 67-70, 発行日 2020-03-15 |
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ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 18824374 | |||||
抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | Since the implementation of school evaluation questionnaires at public elementary and junior high schools has become compulsory, teachers have been burdened with counting questionnaires. On the other hand, in public schools, it is difficult to purchase a mark sheet reading device and mark sheet paper due to budget constraints. In this study, we investigate a method of totalizing questionnaires using a commercially available scanner using questionnaire paper that can be printed with a general printer. We propose a method using deep learning for check mark recognition, and show that sufficient recognition accuracy can be obtained by learning about 1000 samples. | |||||
フォーマット | ||||||
内容記述タイプ | Other | |||||
内容記述 | application/pdf |