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This paper presents a novel processing pipeline that reconstructs online handwriting trajectories from sensor signals of a digital pen, addressing the challenge of differing sampling rates between the pen and the tablet. By employing Dynamic Time Warping for signal alignment and a Temporal Convolutional Network for trajectory reconstruction, the authors achieve significant improvements over existing methods. The proposed approach is validated on a new benchmark dataset, demonstrating enhanced performance in both qualitative and quantitative evaluations.
Achieving superior handwriting trajectory reconstruction from kinematic sensors could revolutionize digital pen applications across education and collaborative environments.
Handwriting with digital pens is a common way to facilitate humanâcomputer 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.
