Write on Paper and Get the Online Digital Trace: A New Era for Handwriting
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 4 of its 12 references that are in the map. First posted on arXiv 11 Sep 2026.
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References · 4
- 2020Neural Architecture Search without Trainingcited by 241
- 2026Mixture-of-experts for handwriting trajectory reconstruction from IMU sensorscited by 1
- 2026Online Handwriting Trajectory Reconstruction from Kinematic Sensors using Temporal Convolutional Networkcited by 1
- 2026Domain adaptation for handwriting trajectory reconstruction from IMU sensorscited by 1
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