# Mixture-of-experts for handwriting trajectory reconstruction from IMU sensors

arXiv: https://arxiv.org/abs/2607.26708 · HTML: https://mlsanity.com/paper/2607.26708/

## Key facts

- Cited by 1 papers in the map (data through 7 Oct 2026).
- Builds on 8 of its 36 references that are in the map.
- First posted on arXiv 29 Jul 2026.

## Builds on (most cited first, 8 of 8)

- [Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift](https://mlsanity.com/paper/2204.03342/) (2022)
- [AGTGAN: Unpaired Image Translation for Photographic Ancient Character Generation](https://mlsanity.com/paper/2303.07012/) (2023)
- [Benchmarking Online Sequence-to-Sequence and Character-based Handwriting Recognition from IMU-Enhanced Pens](https://mlsanity.com/paper/2202.07036/) (2022)
- [Complex Handwriting Trajectory Recovery: Evaluation Metrics and Algorithm](https://mlsanity.com/paper/2210.15879/) (2022)
- [Online trajectory recovery from offline handwritten Japanese kanji characters](https://mlsanity.com/paper/2009.04284/) (2020)
- [Uncertainty-aware Evaluation of Time-Series Classification for Online Handwriting Recognition with Domain Shift](https://mlsanity.com/paper/2206.08640/) (2022)
- [Detecting Handwritten Mathematical Terms with Sensor Based Data](https://mlsanity.com/paper/2109.05594/) (2021)
- [Improving Accuracy and Explainability of Online Handwriting Recognition](https://mlsanity.com/paper/2209.09102/) (2022)

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

- [Write on Paper and Get the Online Digital Trace: A New Era for Handwriting](https://mlsanity.com/paper/2609.12702/) (2026)

## Abstract (arXiv)

The use of digital pens for online handwriting trajectory reconstruction is a prevalent method for human-computer interaction. In this study, we focus on a digital pen equipped with sensors where we aim at reconstructing the online handwriting trajectory. This pen enables writing on any surface and preserving the digital trace of handwriting. This type of pen could be used as an aid to learning to write in classroom. In this paper, we propose a new approach learning to finely reconstruct the touching trajectories while precisely analyzing the hovering part in order to position the next touching trace correctly. This relies on a Mixture-Of-Experts (MOE) approach. The first expert is dedicated for the pencil touch, and is named touching expert model. The second one is dedicated for the hovering pen trajectory, and is named hovering expert model. We improve on the learning of each of these experts based on additional context or specific examples. In addition we introduce a novel public benchmark dataset, to enable future research and comparisons in the field of handwriting reconstruction. The results demonstrates a significant enhancement compared to its primary competitors.