arXiv:2607. 09826v1 Announce Type: cross Abstract: Dysgraphia is a specific learning disability that is prevalent among school-age children.
arXiv:2607. 26736v1 Announce Type: new Abstract: Digital pens are commonly used to write on digital devices, providing the handwriting trace and enhancing human-computer interation.
Teaching machines to emulate natural handwriting styles remains an open challenge, as it requires synthesizing stroke sequences that dynamically vary in shape, texture, pressure and script - not only across individuals, but also within a single person's handwriting. Attempts at this challenge have largely explored deep learning methods in both online and offline settings.
Digital pens are commonly used to write on digital devices, providing the handwriting trace and enhancing human-computer interation. This study focuses on a digital pen equipped with kinematic sensors, allowing users to write on any surface while simultaneously preserving a digital trajectory of handwriting.
arXiv:2607. 26733v1 Announce Type: cross Abstract: Handwriting with digital pens is a common way to facilitate human-computer interaction through the use of Online Handwriting (OH) trajectory reconstruction.
arXiv:2512. 16184v2 Announce Type: replace Abstract: Early and accessible detection of Alzheimer's disease (AD) remains a critical clinical challenge, and cube-copying tasks offer a simple yet informative assessment of visuospatial function.
arXiv:2607. 12500v1 Announce Type: new Abstract: Deep learning models for online handwriting recognition have been shown effective and are increasingly deployed in practical applications.
arXiv:2606. 24984v1 Announce Type: new Abstract: Learning representations that remain robust across centuries of variation in handwriting is a key challenge in diachronic representation learning.
arXiv:2603. 16883v2 Announce Type: replace-cross Abstract: Inertial measurement unit-based online handwriting recognition enables the recognition of input signals collected across different writing surfaces but remains challenged by uneven character distributions and inter-writer variability.
arXiv:2607. 04820v1 Announce Type: new Abstract: Decoding hand kinematics from surface electromyography (EMG) is a core challenge in wearable biosignal processing with clinical relevance for prosthetic control and motor rehabilitation.
arXiv:2608. 09830v1 Announce Type: new Abstract: Body-focused repetitive behaviors, such as hair pulling and skin picking, are compulsive motor actions commonly associated with obsessive-compulsive and anxiety disorders.
arXiv:2607. 26708v1 Announce Type: new Abstract: The use of digital pens for online handwriting trajectory reconstruction is a prevalent method for human-computer interaction.
arXiv:2606. 08858v1 Announce Type: cross Abstract: The automatic processing of handwritten forms remains a challenging task, wherein detection and subsequent classification of handwritten characters are essential steps.