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arXiv · 2606.02342

Detecting Pen-In-Air States from Video: A Proof-of-Concept Toward Complementary Handwriting Analysis

Lauren Sismeiro·Remy Plastre·Binbin Xu·Frederic Puyjarinet·Gerard Dray
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Abstract

Dynamic aspects of handwriting are critical for assessing developmental disorders such as dysgraphia and are typically captured using digitizing tablets. However, tablet-based sensing restricts analysis of Pen-Up behavior to a short proximity range above the writing surface, potentially missing high-lift in-air movements. As a proof of concept, we investigate whether top-view video can provide a complementary source of information for inferring pen-contact states without relying on tablet proximity sensing. We propose an interpretable hybrid pipeline combining pen-tip tracking using a YOLO-based detector with kinematic feature extraction and machine learning classification. A pilot dataset of diverse handwriting videos was manually annotated at the frame level and evaluation used a Leave-One-Video-Out (LOVO) protocol. The method achieved reliable event-level detection of Pen-Up segments, with an F_2 score up to 0.805, consistent with the emphasis on recall in a screening-oriented setting. These results support the feasibility of video-based Pen-Up detection as a low-cost and non-intrusive complement to digitizing tablets, and provide a foundation for future large-scale studies.

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Lauren Sismeiro, Remy Plastre, Binbin Xu, Frederic Puyjarinet, Gerard Dray. 2026-06-01. Detecting Pen-In-Air States from Video: A Proof-of-Concept Toward Complementary Handwriting Analysis. https://arxiv.org/abs/2606.02342

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