用传感器数据重建手写轨迹，提升数字笔书写识别精度。
Online Handwriting Trajectory Reconstruction from Kinematic Sensors using Temporal Convolutional Network
- 通过动态时间规整对齐笔与平板采样率差异，提升信号匹配度。
- 基于时序卷积网络的模型使轨迹重建误差显著低于现有方法。
- 适用于书写教学、协作会议等场景，适合数字笔研发者参考。
使用数字笔进行手写是人机交互的常见方式，其核心在于在线手写（OH）轨迹重建。本文针对配备传感器的数字笔，提出一种新处理流程，将笔端传感器信号映射为对应的在线手写轨迹。为解决笔与平板之间采样率不一致的问题，预处理阶段采用动态时间规整（Dynamic Time Warping）对齐信号。设计了一种受时序卷积网络启发的专用神经网络架构，用于从传感器信号中重建手写轨迹。此外，本文还构建了一个新的基准数据集，所提方法在定性和定量评估中均显著优于当前最先进方法。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。