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: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:2609.12702v2 Announce Type: replace Abstract: Capturing the digital trace of handwriting usually requires a specific stylus and a compatible substrate, be it a capacitive touchscreen, an Electr...
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: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.
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.
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.
arXiv:2609.17565v1 Announce Type: new Abstract: Online handwriting recognition systems typically represent pen trajectories through fixed-length Euclidean shape descriptors that capture the spatial o...
Teaching machines to emulate natural handwriting styles remains an open challenge, as it requires synthesizing stroke sequences that dynamically vary in shape, texture, pressure and script - not only across individuals, but also within a single person's handwriting. Attempts at this challenge have largely explored deep learning methods in both online and offline settings.
arXiv:2608. 06221v1 Announce Type: cross Abstract: Learning from demonstration (LfD) provides a developmental framework through which robots can develop motor skills by observing and imitating human dynamics, reducing reliance on explicit programming to teach a skill to a robot.
arXiv:2604.02103v3 Announce Type: replace-cross Abstract: Realistic online handwriting depends not only on individual character shapes, but also on how a writer connects, spaces, and aligns adjacent...
The paper introduces NormPaST‑Risk, a novel framework that detects Alzheimer’s disease from online handwriting by focusing on local, segment‑level risk rather than whole‑trajectory features. It employs a multi‑scale temporal encoder, a Paper‑Air state‑space model to separate on‑paper motor execution from in‑air planning, and a healthy‑normative branch to learn normal handwriting dynamics. A weakly supervised segment‑risk module identifies high‑risk handwriting segments, achieving superior AD/HC classification on the DARWIN benchmark and offering interpretable evidence linked to disease‑related handwriting changes.
Learning from demonstration (LfD) provides a developmental framework through which robots can develop motor skills by observing and imitating human dynamics, reducing reliance on explicit programming to teach a skill to a robot. The resulting human-like robot motion is recognised as a key factor in building trust and enabling natural collaboration in human-robot interaction.