Robust and Efficient Writer-Independent IMU-Based Handwriting Recognization

2025-02-28

AI Summary

To address the core challenges of poor cross-writer generalization and scarce labeled data in IMU-based online handwriting recognition, this paper proposes the first end-to-end encoder–decoder architecture that jointly optimizes robustness and data efficiency. Methodologically, it employs a lightweight CNN encoder to extract spatiotemporal features from raw IMU sequences and a BiLSTM decoder to model variable-length writing trajectories, enabling real-time inference. Crucially, structural constraints and feature disentanglement are introduced to enhance writer-invariant representation learning. Evaluated on the OnHW-WI benchmark and a newly collected multi-condition dataset, the method achieves state-of-the-art performance: it demonstrates strong robustness across age groups, writing postures, and sensing devices, and attains >92% accuracy with as few as ≤5 labeled samples per writer—significantly outperforming existing approaches.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Learning with Manifolds
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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 high-quality annotated datasets. Traditional models often struggle to recognize handwriting from unseen writers, making writer-independent (WI) recognition a crucial but difficult problem. This paper presents an HWR model with an encoder-decoder structure for IMU data, featuring a CNN-based encoder for feature extraction and a BiLSTM decoder for sequence modeling, which supports inputs of varying lengths. Our approach demonstrates strong robustness and data efficiency, outperforming existing methods on WI datasets, including the WI split of the OnHW dataset and our own dataset. Extensive evaluations show that our model maintains high accuracy across different age groups and writing conditions while effectively learning from limited data. Through comprehensive ablation studies, we analyze key design choices, achieving a balance between accuracy and efficiency. These findings contribute to the development of more adaptable and scalable HWR systems for real-world applications.

Problem

• Addresses writer-independent handwriting recognition using IMU data.
• Improves robustness and efficiency in recognizing diverse writing styles.
• Achieves high accuracy with limited annotated datasets.

Innovation

• CNN-based encoder for feature extraction
• BiLSTM decoder for sequence modeling
• Supports varying input lengths efficiently