# Write on Paper and Get the Online Digital Trace: A New Era for Handwriting

arXiv: https://arxiv.org/abs/2609.12702 · HTML: https://mlsanity.com/paper/2609.12702/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 11 Sep 2026.

## Builds on (most cited first, 4 of 4)

- [Neural Architecture Search without Training](https://mlsanity.com/paper/2006.04647/) (2020)
- [Mixture-of-experts for handwriting trajectory reconstruction from IMU sensors](https://mlsanity.com/paper/2607.26708/) (2026)
- [Domain adaptation for handwriting trajectory reconstruction from IMU sensors](https://mlsanity.com/paper/2607.26736/) (2026)
- [Online Handwriting Trajectory Reconstruction from Kinematic Sensors using Temporal Convolutional Network](https://mlsanity.com/paper/2607.26733/) (2026)

## Built on by (most cited first, 1 of 1)

- [Age-Adaptive Handwriting Reconstruction from an IMU-Based Digital Pen through Shared Representations and Domain-Specific Heads](https://mlsanity.com/paper/2609.31666/) (2026)

## 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.