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Communication Dans Un Congrès Année : 2024

Domain adaptation for handwriting trajectory reconstruction from IMU sensors

Florent Imbert
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Romain Tavenard
Yann Soullard
Eric Anquetil

Résumé

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. This technology holds significant potential as a valuable educational tool, particularly in classrooms where it can facilitate the process of learning to write. A major issue is based on the difference in captured signals between adults and children. For similar handwriting trace, we have large differences in sensor signals due to differences in speed and confidence in the handwriting gesture of children. To address this, we investigate a domain adaptation approach to build a unified intermediate feature representation aimed at facilitating the trajectory reconstruction. We demonstrate the interest of domain adaptation methods in leveraging existing knowledge for application in different contexts. Specifically, we compare our domain adaptation approach with two other methods: training the model from scratch and fine-tuning the model.
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Dates et versions

hal-04605593 , version 1 (07-06-2024)

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  • HAL Id : hal-04605593 , version 1

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Florent Imbert, Romain Tavenard, Yann Soullard, Eric Anquetil. Domain adaptation for handwriting trajectory reconstruction from IMU sensors. ICDAR 2024 Workshops, ADAPDA, Aug 2024, Athènes, Greece. ⟨hal-04605593⟩
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