inkGround is a new benchmark for evaluating how well Vision‑Language Models can recover the fine‑grained stroke trajectories of cursive handwriting from camera‑captured page images. It contains 537 full‑page images with 18.7k words and 163k strokes, paired with pseudo‑trajectory references generated by an offline‑to‑online model and verified by humans. The benchmark casts the task as text‑conditioned point grounding, scoring models on trajectory fidelity, readability, and style similarity, and compares both open‑source and closed‑source VLMs, showing that closed‑source models like Gemini‑3.1‑Pro perform best.
By Sidra Hanif
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
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
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:2609.37195v1 Announce Type: new
Abstract: Handwritten Text Recognition (HTR) systems have become an indispensable tool for the digitization of historical documents. Not only do they cut down ti...
By Eric Ayllon, Abel Gandia, Jorge Calvo-Zaragoza
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