arXiv Machine Learning
Write on Paper and Get the Online Digital Trace: A New Era for Handwriting
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arXiv Machine Learning
Sep 14
Write on Paper and Get the Online Digital Trace:\newline A New Era for Handwriting
The paper introduces a new method for capturing the digital trace of handwriting written on regular paper. It combines a sensor-equipped digital pen with advanced AI algorithms to reconstruct the pen’s trajectory in real time, eliminating the need for specialized styluses or substrates. The approach integrates hardware development and embedded AI software to enable seamless digitization of traditional handwriting.
arXiv Machine Learning
Jul 30
Mixture-of-experts for handwriting trajectory reconstruction from IMU sensors
arXiv:2607. 26708v1 Announce Type: new Abstract: The use of digital pens for online handwriting trajectory reconstruction is a prevalent method for human-computer interaction.
arXiv Machine Learning
Jul 30
Online Handwriting Trajectory Reconstruction from Kinematic Sensors using Temporal Convolutional Network
arXiv:2607. 26733v1 Announce Type: cross Abstract: Handwriting with digital pens is a common way to facilitate human-computer interaction through the use of Online Handwriting (OH) trajectory reconstruction.
arXiv Machine Learning
Jul 30
Domain adaptation for handwriting trajectory reconstruction from IMU sensors
arXiv:2607. 26736v1 Announce Type: new Abstract: Digital pens are commonly used to write on digital devices, providing the handwriting trace and enhancing human-computer interation.
Hugging Face Trending Papers
Jul 29
Domain adaptation for handwriting trajectory reconstruction from IMU sensors
Digital pens are commonly used to write on digital devices, providing the handwriting trace and enhancing human-computer interation. This study focuses on a digital pen equipped with kinematic sensors, allowing users to write on any surface while simultaneously preserving a digital trajectory of handwriting.
arXiv Computer Vision
Sep 3
Handwriting Trajectory Recovery via Autoregressive Ordered Stroke Instance Prediction
The paper presents a two‑stage method for recovering handwriting trajectories from static images. First, it predicts ordered stroke instances autoregressively, then reconstructs continuous motion within each stroke using direction‑related cues. Experiments on Chinese, English, and Tamil handwriting show that this ordered prediction outperforms post‑hoc ordering and baseline models, and that sampling density significantly impacts performance.