The paper presents a two‑stage method for recovering handwriting trajectories from static images. First, it predicts ordered stroke instances autoregressively, then reconstructs continuous motion within each stroke using direction‑related cues. Experiments on Chinese, English, and Tamil handwriting show that this ordered prediction outperforms post‑hoc ordering and baseline models, and that sampling density significantly impacts performance.
arXiv:2609.17565v1 Announce Type: new Abstract: Online handwriting recognition systems typically represent pen trajectories through fixed-length Euclidean shape descriptors that capture the spatial o...
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: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: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.
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:2604.02103v3 Announce Type: replace-cross Abstract: Realistic online handwriting depends not only on individual character shapes, but also on how a writer connects, spaces, and aligns adjacent...
The paper introduces NormPaST‑Risk, a novel framework that detects Alzheimer’s disease from online handwriting by focusing on local, segment‑level risk rather than whole‑trajectory features. It employs a multi‑scale temporal encoder, a Paper‑Air state‑space model to separate on‑paper motor execution from in‑air planning, and a healthy‑normative branch to learn normal handwriting dynamics. A weakly supervised segment‑risk module identifies high‑risk handwriting segments, achieving superior AD/HC classification on the DARWIN benchmark and offering interpretable evidence linked to disease‑related handwriting changes.
The paper presents a rotation‑free online handwritten character recognition system that uses Sliding Window Path Signature (SW‑PS) to extract local structural features and a lightweight Linear Recurrent Unit (LRU) classifier. The LRU blends the incremental processing of RNNs with the parallel training efficiency of state‑space models to model dynamic stroke characteristics. Experiments on rotated CASIA‑OLHWDB1.1 subsets (digits, English upper letters, Chinese radicals) achieved accuracies of 99.62%, 96.67%, and 94.33% respectively, outperforming competing models in convergence speed and test accuracy.
arXiv:2609.12702v2 Announce Type: replace Abstract: Capturing the digital trace of handwriting usually requires a specific stylus and a compatible substrate, be it a capacitive touchscreen, an Electr...
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.
The paper introduces a new method for capturing the digital trace of handwriting written on regular paper. It combines a sensor-equipped digital pen with advanced AI algorithms to reconstruct the pen’s trajectory in real time, eliminating the need for specialized styluses or substrates. The approach integrates hardware development and embedded AI software to enable seamless digitization of traditional handwriting.
arXiv:2608.30689v1 Announce Type: new Abstract: Autoregressive large models have recently advanced Text-to-SVG generation from simple icons to complex, long-context graphics, yet standard autoregress...