发表机构
IRISA, Université de Rennes, INSA Rennes; IRISA, Université Rennes 2; LETG（IRISA研究所、雷恩大学、雷恩国立应用科学学院; IRISA研究所、雷恩第二大学; LETG机构）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对数字笔与数位板采样率差异问题，结合动态时间规整与时间卷积网络架构，构建新基准数据集，实现从运动传感器信号到在线手写轨迹的高精度重建。
AI 中文摘要
使用数字笔进行手写是通过在线手写（OH）轨迹重建实现人机交互的常见方式。本研究聚焦于配备传感器的数字笔，旨在从该笔的传感器信号中重建OH轨迹。这种笔可在任意表面书写并获取数字轨迹，既有助于通过纸质书写学习写字，也适用于协作会议等诸多场景。本文提出一种新型处理流程，将数字笔的传感器信号映射至对应的OH轨迹。特别地，为解决笔与提供真值信息的数位板之间采样率差异问题，预处理流程采用动态时间规整（Dynamic Time Warping）来对齐信号。我们引入一种受时间卷积网络（Temporal Convolutional Network）启发的专用神经网络架构，用于从笔传感器信号重建在线轨迹。最后，我们还提供了一个新的基准数据集，在该数据集上对所提方法进行定性与定量评估，结果显示其相较于最具竞争力的方法有显著提升。
英文摘要
Handwriting with digital pens is a common way to facilitate human-computer interaction through the use of Online Handwriting (OH) trajectory reconstruction. In this work, we focus on a digital pen equipped with sensors from which one wants to reconstruct the OH trajectory. Such a pen allows to write on any surface and to get the digital trace, which can help learning to write, by writing on paper, and can be useful for many other applications such as collaborative meetings, etc. In this paper, we introduce a novel processing pipeline that maps the sensor signals of the pen to the corresponding OH trajectory. Notably, in order to tackle the difference of sampling rates between the pen and the tablet (which provides ground truth information), our preprocessing pipeline relies on Dynamic Time Warping to align the signals. We introduce a dedicated neural network architecture, inspired by a Temporal Convolutional Network, to reconstruct the online trajectory from the pen sensor signals. Finally, we also present a new benchmark dataset on which our method is evaluated both qualitatively and quantitatively, showing a notable improvement over its most notable competitor.
Journal refInternational Journal on Document Analysis and Recognition (IJDAR) May 2023
DOI：10.1007/s10032-023-00430-1
