Mixture-of-experts for handwriting trajectory reconstruction from IMU sensors

AI 中文解读

这篇论文提出了一种让数字笔“更聪明”的新方法——用混合专家模型分别解析笔尖接触和空中悬停两个阶段，从而更精准地还原手写轨迹。过去这类笔主要关注笔尖落笔的路径，但这次研究人员给笔加了一个“悬空专家”，专门分析笔在空中移动的轨迹，这样写下一个字时位置能自动对齐，整体书写效果更连贯自然。这套技术让数字笔无需专用纸板，在任何表面书写都能保留完整电子记录，尤其适合课堂辅助学写字：老师能实时看到学生的书写过程，包括顿笔、提笔的细微动作，从而针对性地纠正姿势和笔顺。研究团队还公开了标准测试数据集，方便后续开发者对比优化。未来这种笔很可能成为智能教室的标配工具，让手写练习变得数据化、个性化。

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.