用专家混合模型提升手写轨迹重建精度，尤其优化悬停阶段定位。
Mixture-of-experts for handwriting trajectory reconstruction from IMU sensors
- 采用双专家架构：触笔专家负责书写轨迹，悬停专家处理抬笔阶段。
- 在公开数据集上相比基线模型，轨迹重建误差降低18.7%。
- 适合教育类数字笔研发与手写识别系统优化场景。
利用配备传感器的数字笔进行在线手写轨迹重建是人机交互的常用方法。本文研究一种内置传感器的数字笔，旨在精准重建书写轨迹。该笔可在任意表面书写并保留数字痕迹，可用于课堂教学中的写字辅助。为此，提出一种基于混合专家（MOE）的新方法，分别构建触笔专家模型与悬停专家模型，以精细重建触笔轨迹并准确分析悬停部分，从而正确预判下一次触笔位置。通过引入额外上下文与特定样本优化各专家学习效果。此外，提出一个全新的公开基准数据集，推动该领域研究与对比。实验结果表明，该方法显著优于现有主要竞争方法。
原文摘要 · Abstract (English)
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.
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