aiqbench analysis
This research proposes a Temporal Convolutional Network architecture to reconstruct digital handwriting trajectories directly from kinematic sensor data, addressing sampling rate mismatches via Dynamic Time Warping. By establishing a new benchmark dataset, the study demonstrates significant performance gains over existing methods in human-computer interaction scenarios involving digital pens and tablets. The work advances machine learning applications within computer vision for interpreting sequential motion signals to generate precise 2D traces from raw sensor inputs.
Online Handwriting Trajectory Reconstruction from Kinematic Sensors using Temporal Convolutional Network
arXiv:2607.26733v1 Announce Type: cross Abstract: 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.
Abstract: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.
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
arXiv:2607.26733 [cs.CV]
(or arXiv:2607.26733v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.26733 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.1007/s10032-023-00430-1 Focus to learn more DOI(s) linking to related resources
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