Research on the Handwriting Trajectory Reconstruction and Recognition with Wearable Sensing Method
| Project/Area Number |
18K11400
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 61020:Human interface and interaction-related
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| Research Institution | The University of Aizu |
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Principal Investigator |
Jing Lei 会津大学, コンピュータ理工学部, 上級准教授 (30595509)
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| Co-Investigator(Kenkyū-buntansha) |
裴 岩 (裴岩) 会津大学, コンピュータ理工学部, 准教授 (30736004)
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| Project Period (FY) |
2018-04-01 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2021: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2020: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2019: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2018: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | 手書き認識 / wearable computing / handwriting tracking / data fusion / データグローブ / ウェアラブルデバイス / tracking / GNN / handwriting / segmentation |
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| Outline of Final Research Achievements |
A Wearable HandWriting Recognition (WHWR) System is developed.
1. Through continuous testing on the prototypes of the WHWR sensor node, we have successfully developed a tiny size wearable sensor node. Moreover, the powering issues, which arose from the deployment constrains on the finger nail, have been solved as well. 2. A database for the handwriting data management is developed, together with a visualization function of the data waveforms and the 3D demonstration on the handwriting trajectory, which greatly improve the efficiency of the data analysis. 3. DTW (CDP) is adopted for the segmentation on the continuous handwriting and kernal SVM is adopted for the handwriting characters recognition, which accuracy is about 97% on a 10 digits data set.
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| Academic Significance and Societal Importance of the Research Achievements |
申請者はICDAR17に提案したウェアラブル手書き認識方式（WHWR）を指先に付けられる手書き動作記録装置、およびそれに適用できる認識手法の研究開発によって、発展させました。
「筆記」は、空間と時代を超えて、人間同士がコミュニケーションするために欠かせない手段である。 WHWRは既存の手書き認識手法と比べて、任意のペン、あるいは指だけで、自由なスペースで入力できること、さらに、アナログおよびデジタルの情報を同時に記録できることが独自の優れた特徴である。WHWRを発展させ、筆記データのシームレスなデジタル化を実現し、新たな筆記方式の創出に貢献することが期待できる。
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Report
(5 results)
Research Products
(29 results)
