发表机构
IRISA; Universite de Rennes; INSA Rennes; Universite Rennes 2（IRISA（法国国家科学研究中心、雷恩第一大学等联合组建的科研机构）; 雷恩第一大学; 雷恩国立应用科学学院; 雷恩第二大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对配备IMU传感器的数字笔手写轨迹重建问题，提出混合专家（MOE）模型，引入公开基准数据集，实现了比主流方法更优的手写轨迹重建效果。
AI 中文摘要
数字笔用于在线手写轨迹重建是人机交互的常用方法，本研究聚焦配备传感器的数字笔，旨在重建在线手写轨迹，该笔可在任意表面书写并保留手写数字痕迹，有望作为课堂学写辅助工具。本文提出一种新方法，通过精确分析悬停部分以正确定位下一个接触轨迹，来精细重建接触轨迹，该方法基于混合专家（Mixture-Of-Experts，MOE）模型：第一个专家模型专门处理笔接触，名为接触专家模型；第二个专家模型专门处理悬停笔轨迹，名为悬停专家模型。我们基于额外上下文或特定示例改进每个专家的学习，此外还引入了一个新颖的公开基准数据集，以支持该领域未来的研究与对比。实验结果表明，与主要竞争方法相比，该方法实现了显著提升。
英文摘要
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.
Journal refPattern Recognition May 2025
DOI：10.1016/j.patcog.2024.111231
