arXiv:2502.20954v3 Announce Type: replace 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
The continuous advancements in AI, especially in robust feature extraction and sequential data processing, enable more sophisticated approaches to challenging tasks like writer-independent handwriting recognition.
Improved IMU-based handwriting recognition can unlock new applications in wearable technology, accessibility, and discreet input methods, reducing dependency on visual or touch-based interfaces.
The demonstrated robustness to unseen writing styles signifies a practical step towards deploying IMU-based handwriting recognition in diverse real-world scenarios without extensive calibration per user.
- · Wearable tech manufacturers
- · Accessibility technology developers
- · Handwriting recognition software companies
- · Augmented reality/virtual reality interfaces
- · Traditional input device manufacturers (niche)
- · Pen and paper (niche substitution)
- · Less robust handwriting recognition systems
More accurate and versatile wearable input methods become available, extending use cases for smartwatches and other devices.
This technology could enable entirely new forms of discreet communication or data entry in environments where traditional input is impractical.
Ubiquitous, implicit input through gestures and handwriting could fundamentally alter human-computer interaction paradigms, moving towards more seamless and intuitive interfaces.
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