Robust and Efficient Writer-Independent IMU-Based Handwriting Recognition
Jindong Li, Tim Hamann, Jens Barth, Peter K\"ampf, Dario Zanca, Bj\"orn Eskofier

TL;DR
This paper introduces a CNN-BiLSTM model for IMU-based handwriting recognition that is highly robust to unseen writers and styles, outperforming existing methods on multiple datasets and demonstrating strong generalization capabilities.
Contribution
The paper proposes a novel CNN encoder and BiLSTM decoder architecture that significantly improves writer-independent IMU handwriting recognition performance and robustness.
Findings
Achieved CER of 7.37% and WER of 15.12% on public dataset
Maintains accuracy across different age groups
Demonstrates effective sentence recognition in full sentences
Abstract
Online handwriting recognition (HWR) using data from inertial measurement units (IMUs) remains challenging due to variations in writing styles and the limited availability of annotated datasets. Previous approaches often struggle with handwriting from unseen writers, making writer-independent (WI) recognition a crucial yet difficult problem. This paper presents an HWR model designed to improve WI HWR on IMU data, using a CNN encoder and a 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 accuracy across different age groups, and knowledge learned…
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Taxonomy
TopicsHandwritten Text Recognition Techniques · Hand Gesture Recognition Systems · Interactive and Immersive Displays
