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Li, J.* ; Hamann, T.* ; Barth, J.* ; Kämpf, P.* ; Zanca, D.* ; Eskofier, B.M.

Robust and Efficient Writer-Independent IMU-Based Handwriting Recognition.

In: (Sensor-Based Activity Recognition and Artificial Intelligence). Berlin [u.a.]: Springer, 2026. 261 - 286 (Lect. Notes Comput. Sc. ; 16292 LNCS)
DOI
Handwriting recognition (HWR) using inertial measurement unit (IMU) data remains challenging due to variations in writing styles and the limited availability of datasets. Previous approaches often struggle with handwriting from unseen writers, making writer-independent (WI) recognition a crucial yet difficult problem. This paper presents a model designed to improve WI HWR on IMU data, using a CNN encoder and BiLSTM-based decoder. Our approach demonstrates strong robustness to unseen handwriting styles, outperforming existing methods on the WI splits of both the public OnHW dataset and our word-based dataset, achieving character error rates (CERs) of 7.37% and 9.44%, and word error rates (WERs) of 15.12% and 32.17%, respectively. Robustness evaluation shows that our model maintains superior performance across different age groups, with knowledge learned from one group generalizing better to another compared to other approaches. Evaluation on our sentence-based dataset further demonstrates the potential for recognizing full sentences. Through comprehensive ablation studies, we show that our design choices achieve a strong balance between performance and efficiency. These findings support the development of more adaptable and scalable HWR systems for real-world applications. The code is available at: https://github.com/jindongli24/REWI.
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Publication type Article: Conference contribution
Keywords Inertial Measurement Unit ; Online Handwriting Recognition ; Time-series Analysis
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Conference Title Sensor-Based Activity Recognition and Artificial Intelligence
Journal Lecture Notes in Computer Science
Quellenangaben Volume: 16292 LNCS, Issue: , Pages: 261 - 286 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Institute(s) Institute of AI for Health (AIH)