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...
By Florent Imbert, Yann Soullard, Eric Anquetil, Tanja Harbaum, Alexey Serdyuk, Fabian Kress, Tim Hamann, Peter Kampf
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.
By Florent Imbert, Eric Anquetil, Yann Soullard, Romain Tavenard
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.
By Wassim Swaileh, Florent Imbert, Yann Soullard, Romain Tavenard, Eric Anquetil
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.
By Florent Imbert, Romain Tavenard, Yann Soullard, Eric Anquetil
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.
By En-Guang Wang, Yan-Ming Zhang, Fei Yin, Cheng-Lin Liu