发表机构
IRISA, Université de Rennes, INSA Rennes; IRISA, Université Rennes 2; Karlsruhe Institute of Technology (KIT); STABILO International GmbH; LTU, Machine Learning Group, Luleå University of Technology（IRISA，雷恩大学，INSA雷恩; IRISA，雷恩第二大学; 卡尔斯鲁厄理工学院; STABILO国际有限公司; 吕勒奥理工大学机器学习组）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出结合数字笔、AI算法与自适应技术，实现普通纸上手写轨迹的实时数字化重建，无需外部参考系统，推动手写数字化的新纪元。
AI 中文摘要
捕获手写的数字轨迹通常需要特定的触控笔和兼容的基板，无论是电容式触摸屏、Wacom系统中使用的电磁共振（EMR）数位板，还是特殊纸张。虽然在普通纸上书写提供了丰富的触感、无延迟，并且众所周知能提高信息保留率，但尚不存在低成本、被广泛接受且有效的解决方案来数字化这种笔迹。挑战在于在没有外部参考系统的情况下准确跟踪笔的轨迹，同时允许笔在表面上不受限制地自由移动。我们提出了一种创新解决方案，结合数字笔、先进的人工智能算法和自适应AI技术，以重建手写的数字轨迹。我们的方法整合了硬件开发（专注于配备传感器的笔）和软件创新，利用嵌入式AI实时优化轨迹重建和处理。这项工作旨在推进手写自动轨迹重建的最新技术水平，实现纸上传统手写与数字轨迹捕获之间的无缝连接。
英文摘要
Capturing the digital trace of handwriting usually requires a specific stylus and a compatible substrate, be it a capacitive touchscreen, an ElectroMagnetic Resonance (EMR) tablet as used in Wacom systems or special paper. While writing on regular paper offers rich haptics, no latency and is well known for improving information retention, no low-cost and widely accepted, effective solution exists to digitize such a pen trace. The challenge is to accurately track the pen's trajectory without an external reference system while allowing unrestricted freedom of pen movement across a surface. We propose an innovative solution that combines a digital pen, advanced artificial intelligence algorithms, and adaptive AI techniques to reconstruct the digital trace of handwriting. Our approach integrates hardware development, focusing on a sensor-equipped pen, with software innovations to optimize trajectory reconstruction and processing in real time using an embedded AI. This work aims to advance the state-of-the-art in automated trace reconstruction of handwriting, enabling a seamless connection between traditional handwriting on paper and capturing the trace digitally.
Journal refConference of the International Graphonomics Society (IGS 2025)
