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Title: FMKit: An In-Air-Handwriting Analysis Library and Data Repository
Hand-gesture and in-air-handwriting provide ways for users to input information in Augmented Reality (AR) and Virtual Reality (VR) applications where a physical keyboard or a touch screen is unavailable. However, understanding the movement of hands and fingers is challenging, which requires a large amount of data and data-driven models. In this paper, we propose an open research infrastructure named FMKit for in-air-handwriting analysis, which contains a set of Python libraries and a data repository collected from over 180 users with two different types of motion capture sensors. We also present three research tasks enabled by FMKit, including in-air-handwriting based user authentication, user identification, and word recognition, and preliminary baseline performance.
Authors:
Award ID(s):
1925709
Publication Date:
NSF-PAR ID:
10201287
Journal Name:
CVPR Workshop on Computer Vision for Augmented and Virtual Reality
Sponsoring Org:
National Science Foundation
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