The OnHW Dataset: Online Handwriting Recognition from IMU-Enhanced Ballpoint Pens with Machine Learning

Ott F, Wehbi M, Hamann T, Barth J, Eskofier B, Mutschler C (2020)

Publication Type: Journal article

Publication year: 2020

Journal

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Association for Computing Machinery (ACM)

Book Volume: 4

Pages Range: 1-20

Issue: 3

DOI: 10.1145/3411842

Abstract

This paper presents a handwriting recognition (HWR) system that deals with online character recognition in real-time. Our sensor-enhanced ballpoint pen delivers sensor data streams from triaxial acceleration, gyroscope, magnetometer and force signals at 100 Hz. As most existing datasets do not meet the requirements of online handwriting recognition and as they have been collected using specific equipment under constrained conditions, we propose a novel online handwriting dataset acquired from 119 writers consisting of 31,275 uppercase and lowercase English alphabet character recordings (52 classes) as part of the UbiComp 2020 Time Series Classification Challenge. Our novel OnHW-chars dataset allows for the evaluations of uppercase, lowercase and combined classification tasks, on both writer-dependent (WD) and writer-independent (WI) classes and we show that properly tuned machine learning pipelines as well as deep learning classifiers (such as CNNs, LSTMs, and BiLSTMs) yield accuracies up to 90 % for the WD task and 83 % for the WI task for uppercase characters. Our baseline implementations together with the rich and publicly available OnHW dataset serve as a baseline for future research in that area.

Authors with CRIS profile

Mohamad Wehbi Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics) Tim Hamann Jens Barth Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics) Björn Eskofier Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics) Christopher Mutschler

Additional Organisation(s)

Machine Learning and Data Analytics Lab

Involved external institutions

Fraunhofer-Institut für Integrierte Schaltungen (IIS) DE Germany (DE)

How to cite

APA:

Ott, F., Wehbi, M., Hamann, T., Barth, J., Eskofier, B., & Mutschler, C. (2020). The OnHW Dataset: Online Handwriting Recognition from IMU-Enhanced Ballpoint Pens with Machine Learning. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4, 1-20. https://doi.org/10.1145/3411842

MLA:

Ott, Felix, et al. "The OnHW Dataset: Online Handwriting Recognition from IMU-Enhanced Ballpoint Pens with Machine Learning." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4 (2020): 1-20.

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