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

Florent Imbert, Yann Soullard, Eric Anquetil, Tanja Harbaum, Alexey Serdyuk, Fabian Kress, Tim Hamann, Peter Kampf

arXiv:2609.12702v2cs.LG

TL;DR

Existing digital handwriting often depends on compatible pens and surfaces, while paper remains preferred in many learning settings. This paper combines an inertial-sensor pen with deep-learning trace reconstruction, and reports that paper-specific training performs better than tablet-trained reconstruction or filtering alone.

  • Problem

    Existing pen-and-tablet systems may be incompatible, and parents and teachers often prefer paper for children's handwriting.

    Source: §II · p. 1

  • Method

    The authors use the Digipen's inertial signals with preprocessing and a deep-learning mixture-of-experts pipeline to reconstruct handwriting traces on paper.

    Source: §I · p. 1, §III · p. 2

  • Results

    Training on paper data significantly reduced mean Fréchet distance, while Kalman filtering tablet signals yielded only marginal improvement.

    Source: §V · p. 4

  • Takeaways & Limitations

    Because acquisition medium changes signal characteristics, reconstruction models benefit from optimization on data from the target medium.

    Source: §V · pp. 3–4

Abstract

from arXiv · show

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.

I. INTRODUCTION

Handwriting on paper and digital devices offer different benefits, and the paper presents a hardware-software system intended to bridge them. Its contribution combines an inertial-sensor pen, deep-learning trace reconstruction, and model optimization for embedding in the pen.

I. INTRODUCTION

  • Writing on paper offers simplicity and availability, while digital handwriting supports sharing, storage, and feedback applications.The introduction also cites studies reporting handwriting benefits for children's learning and knowledge acquisition compared with keyboard typing,.Source: §I · p. 1
  • The paper combines a sensor-equipped pen, deep-learning trace reconstruction, and AI optimization to embed the technology in the pen.The system distributes complexity between hardware and software to support efficient, fast AI execution.Source: §I · p. 1

II. DIGIPEN: A DIGITAL PEN FOR TRAJECTORY TRACKING

The Digipen is designed to write on paper while collecting inertial data, addressing incompatibility among existing pen-and-tablet technologies. The work moves from handwriting recognition toward trace reconstruction from inertial signals and develops a dual-acquisition setup for training data.

II. DIGIPEN: A DIGITAL PEN FOR TRAJECTORY TRACKING

  • Low-cost tablets often lack compatible pens, while parents and teachers generally prefer children to learn handwriting on paper.The authors identify a need for a pen that writes on paper and interacts with all tablets.Source: §II · p. 1
  • The Digipen records three-axis acceleration and rotation rates plus writing-tip force, and works as a regular pen on regular paper.It also has internal data processing and a BLE link to mobile devices; Fig. 1 shows the experimental pen with a piggybacked battery.Source: §II · p. 1, Figure 1 · p. 2
  • Because children's developing writing may not reliably produce overlearned movement patterns, the authors shifted from pattern-matching recognition to reconstructing traces from inertial data.The paper notes that this reconstruction requires additional training-sample acquisition, unlike an earlier IMU pen that did not reconstruct traces.Source: §II · pp. 1–2
  • A dual-acquisition pen records inertial signals and Wacom tablet trajectories while writing on paper to provide training ground truth.The recordings are resampled and synchronized using tip-force onset as a clapperboard; Fig. 2 describes DTW alignment of IMU and tablet-trajectory frames [11].Source: §II · p. 2, Figure 2 · p. 2

III. DEEP NEURAL NETWORKS FOR HANDWRITING TRACE RECONSTRUCTION FROM IMU SENSORS

The paper addresses handwriting-trace reconstruction from Digipen IMU signals during writing on paper. It uses preprocessing and a deep-learning mixture-of-experts approach to handle the signals and reconstruct digital traces.

III. DEEP NEURAL NETWORKS FOR HANDWRITING TRACE RECONSTRUCTION FROM IMU SENSORS

  • Tablet-and-pen systems often require a specific pen and do not support paper, while Anoto-style camera tools require specialized microdot paper and a costly stylus.Offline-to-online conversion can also lose complex stroke dynamics, or ductus,.Source: §III · p. 2
  • IMUs offer an affordable, low-power, versatile alternative for paper-and-pen capture, but their signals are inherently noisy.The passage also notes their demonstrated compatibility with digital tablets and traditional handwriting setups.Source: §III · p. 2
  • The paper reconstructs handwriting traces from IMU signals recorded while writing on paper with the Digipen.Its pipeline uses advanced preprocessing and a deep-learning mixture-of-experts approach.Source: §III · p. 2

A. Related Works

Prior work has explored neural-network reconstruction from Digipen sensor data, including CNN-based approaches and a DTW-aligned pipeline. The authors build on prior mixture-of-experts work, while noting that earlier experiments used tablets.

A. Related Works

  • Wehbi et al. extend Ott et al.'s monowriter approach to multiple writers and use a CNN with linear interpolation to align sensor data and tablet ground truth,.Their work used handwriting on a tablet with an earlier Digipen version.Source: A. Related Works · p. 2
  • A later pipeline used Dynamic Time Warping for sensor-to-ground-truth alignment and a TCN-inspired neural network; prior work by the authors designed separate experts for touching strokes and pen-up movements,.The two experts address specific signal characteristics.Source: A. Related Works · p. 2
  • Domain-adversarial neural networks have also been explored for children's handwriting across differing levels of graphomotor development.The cited prior experiments were conducted on tablets.Source: A. Related Works · p. 2

B. Dealing with handwriting on paper

Paper’s higher friction adds resistance and noise to IMU signals, challenging models trained only on tablet data. The section compares filtering tablet signals with training on paper data to address this mismatch.

B. Dealing with handwriting on paper

IV. HARDWARE-AWARE NEURAL NETWORK COMPRESSION

The section uses hardware-aware architecture search to compress the TCN experts for deployment on the Digipen’s resource-constrained hardware. The selected architecture sharply reduces model size while retaining comparable reconstruction accuracy.

IV. HARDWARE-AWARE NEURAL NETWORK COMPRESSION

  • The original MOE-CI model requires 3.6 MB ROM and 288 KB RAM, exceeding a typical pen SoC’s 1 MB ROM and 256 KB RAM limits.Source: §IV · p. 3
  • Hardware-aware multiobjective Bayesian optimization searches TCN architectures by maximizing NASWOT score and minimizing FLOPs, with architectures trained and evaluated on hardware.The search space varies convolutional filter-block counts as well as kernel size, dilation, stacks, and layers per stack.Source: §IV · p. 3, Figure 3 · p. 3
  • The best discovered architecture uses 10,952 parameters versus 467,452 in the initial TCN expert, with 74 KB RAM and 66 KB ROM and comparable reconstruction accuracy.Source: §IV · p. 3

V. RESULTS

Paper-specific training improves reconstruction over tablet-only training, while Kalman filtering yields only marginal gains and compression degrades overall trajectory reconstruction. Results are assessed with mean Fréchet distance and illustrated with example traces.

V. RESULTS

  • Tablet-only training generalizes poorly to paper because acquisition-medium signal differences cause considerable reconstruction degradation.Fig. 4 provides qualitative examples of ground-truth and reconstructed traces across models and strategies.Source: §V · p. 3, Figure 4 · p. 4
  • Kalman filtering smooths signals and mitigates high-frequency noise, but its mean Fréchet-distance improvement is marginal and does not bridge the tablet–paper gap.Source: §V · p. 4
  • Training on paper data significantly reduces mean Fréchet distance and improves reconstruction; Table I reports the quantitative comparisons.Source: §V · pp. 3–4, Table I · p. 4
  • Hardware-aware compression significantly increases mean Fréchet distance, with degradation concentrated in pen-up repositioning while pen-touch traces remain quite close to the non-optimized model.Source: §V · p. 4

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