arXiv Computer Vision
A Heisenberg Lift Descriptor for Order Sensitive Online Handwriting Recognition
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arXiv Computer Vision
Sep 3
Handwriting Trajectory Recovery via Autoregressive Ordered Stroke Instance Prediction
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 Computer Vision
Sep 11
Rethinking Handwritten Character Recognition
The paper introduces GraphemeNet, a unified multi‑script handwritten character recognition architecture that explicitly encodes script‑geometric regularities. It uses two orthogonal binary axes: Persistent Scaffold Injection (PSI) to embed stroke‑level geometry into each encoder stage, and a choice between gated global pooling or a Stroke Topology Module for spatial relational reasoning. Across fourteen benchmarks in eight writing systems, GraphemeNet achieves state‑of‑the‑art performance with fewer parameters, demonstrating the effectiveness of structural‑prior efficiency for multi‑script HCR.
arXiv Machine Learning
Sep 22
BoundInk: Boundary-Aware Online Handwriting Generation
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...
arXiv Machine Learning
Jul 15
Adversarial Attacks on Online Handwriting using Salience-based Temporal Editing
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 Machine Learning
Jul 30
Online Handwriting Trajectory Reconstruction from Kinematic Sensors using Temporal Convolutional Network
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 Machine Learning
Sep 10
Rotation-free Online Handwritten Character Recognition Using Linear Recurrent Units
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.