Robust and Efficient Writer-Independent IMU-Based Handwriting Recognition

Li J, Hamann T, Barth J, Kaempf P, Zanca D, Eskofier B (2026)

Publication Language: English

Publication Type: Conference contribution, Original article

Publication year: 2026

Publisher: Springer International Publishing

Series: Lecture Notes in Computer Science

City/Town: Cham, Switzerland

Book Volume: 16292

Pages Range: 261–286

Conference Proceedings Title: International Workshop on Sensor-Based Activity Recognition and Artificial Intelligence

Event location: Drienerlolaan 5, 7522 NB Enschede, NETHERLANDS NL

ISBN: 978-3-032-13311-3

URI: https://link.springer.com/chapter/10.1007/978-3-032-13312-0_16

DOI: 10.1007/978-3-032-13312-0_16

Abstract

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.

Authors with CRIS profile

Jindong Li Machine Learning and Data Analytics Lab Dario Zanca Machine Learning and Data Analytics Lab Björn Eskofier Department Artificial Intelligence in Biomedical Engineering (AIBE)

Involved external institutions

STABILO International GmbH DE Germany (DE)

How to cite

APA:

Li, J., Hamann, T., Barth, J., Kaempf, P., Zanca, D., & Eskofier, B. (2026). Robust and Efficient Writer-Independent IMU-Based Handwriting Recognition. In Özlem Durmaz Incel, Jingwen Qin, Gerald Bieber, Arjan Kuijper (Eds.), International Workshop on Sensor-Based Activity Recognition and Artificial Intelligence (pp. 261–286). Drienerlolaan 5, 7522 NB Enschede, NETHERLANDS, NL: Cham, Switzerland: Springer International Publishing.

MLA:

Li, Jindong, et al. "Robust and Efficient Writer-Independent IMU-Based Handwriting Recognition." Proceedings of the iWOAR 2025 - 10th international Workshop on Sensor-Based Activity Recognition and Artificial Intelligence, Drienerlolaan 5, 7522 NB Enschede, NETHERLANDS Ed. Özlem Durmaz Incel, Jingwen Qin, Gerald Bieber, Arjan Kuijper, Cham, Switzerland: Springer International Publishing, 2026. 261–286.

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