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.

使用数字笔手写是通过使用在线手写 (OH) 轨迹重建来促进人机交互的常用方法。在这项工作中，我们专注于配备传感器的数字笔，人们希望从中重建 OH 轨迹。这种笔允许在任何表面上书写并获得数字痕迹，这可以通过在纸上书写来帮助学习书写，并且可以用于协作会议等许多其他应用。在本文中，我们介绍了一种将笔的传感器信号映射到相应的 OH 轨迹的新型处理管道。值得注意的是，为了解决笔和数位板（提供地面实况信息）之间采样率的差异，我们的预处理管道依赖于动态时间扭曲来对齐信号。我们引入了一种受时间卷积网络启发的专用神经网络架构，以根据笔传感器信号重建在线轨迹。最后，我们还展示了一个新的基准数据集，我们的方法在该数据集上进行了定性和定量评估，显示出比其最著名的竞争对手有显着改进。