aiqbench analysis
This research applies a Mixture-of-Experts architecture to reconstruct handwriting trajectories from IMU sensors by distinguishing between touch and hover states. By introducing a novel public benchmark dataset, the study establishes a new standard for evaluating models in human-computer interaction scenarios involving digital pens. The approach demonstrates significant performance gains over existing methods, offering valuable insights into specialized machine learning applications for educational tools.
Mixture-of-experts for handwriting trajectory reconstruction from IMU sensors
arXiv:2607.26708v1 Announce Type: new Abstract: The use of digital pens for online handwriting trajectory reconstruction is a prevalent method for human-computer interaction. In this study, we focus on a digital pen equipped with sensors where we aim at reconstructing the online handwriting trajectory. This pen enables writing on any surface and preserving the digital trace of handwriting. This type of pen could be used as an aid to learning to write in classroom. In this paper, we propose a new approach learning to finely reconstruct the touching trajectories while precisely analyzing the hovering part in order to position the next touching trace correctly. This relies on a Mixture-Of-Experts (MOE) approach. The first expert is dedicated for the pencil touch, and is named touching expert model. The second one is dedicated for the hovering pen trajectory, and is named hovering expert model. We improve on the learning of each of these experts based on additional context or specific examples. In addition we introduce a novel public benchmark dataset, to enable future research and comparisons in the field of handwriting reconstruction. The results demonstrates a significant enhancement compared to its primary competitors.
Abstract:The use of digital pens for online handwriting trajectory reconstruction is a prevalent method for human-computer interaction. In this study, we focus on a digital pen equipped with sensors where we aim at reconstructing the online handwriting trajectory. This pen enables writing on any surface and preserving the digital trace of handwriting. This type of pen could be used as an aid to learning to write in classroom. In this paper, we propose a new approach learning to finely reconstruct the touching trajectories while precisely analyzing the hovering part in order to position the next touching trace correctly. This relies on a Mixture-Of-Experts (MOE) approach. The first expert is dedicated for the pencil touch, and is named touching expert model. The second one is dedicated for the hovering pen trajectory, and is named hovering expert model. We improve on the learning of each of these experts based on additional context or specific examples. In addition we introduce a novel public benchmark dataset, to enable future research and comparisons in the field of handwriting reconstruction. The results demonstrates a significant enhancement compared to its primary competitors.
Machine Learning (cs.LG)
arXiv:2607.26708 [cs.LG]
(or arXiv:2607.26708v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.26708 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.1016/j.patcog.2024.111231 Focus to learn more DOI(s) linking to related resources
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