BoundInk: Boundary-Aware Online Handwriting Generation

arXiv:2604.02103 · cs.CV, cs.LG · Submitted 2026-04-02 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-04-02

Updated: 2026-09-20

Comments: 22 pages. Major revision. Renamed from "CASHG: Context-Aware Stylized Online Handwriting Generation" to "BoundInk: Boundary-Aware Online Handwriting Generation". The manuscript has been substantially revised with explicit inter-character boundary modeling, boundary-aware evaluation metrics, expanded experiments, and updated appendices

Code: https://github.com/jinsu0000/cashg-official

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 24× in this paper · explained in 2 episodes
• Transformer — 21× in this paper · explained in 2 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Aggregation — 7× in this paper · explained in 1 episode
• Connectivity and Spacing Metrics — 7× in this paper
• Alignment — 6× in this paper · explained in 1 episode
• Robustness — 5× in this paper · explained in 5 episodes
• alpha — 4× in this paper · explained in 1 episode
• Consistency — 3× in this paper · explained in 3 episodes
• Fidelity — 3× in this paper · explained in 2 episodes
• Pairwise Comparisons — 3× in this paper · explained in 2 episodes
• Sparsity — 3× in this paper · explained in 4 episodes
• Vertical Drift Loss — 3× in this paper
• Adaptation — 2× in this paper · explained in 1 episode
• Embeddings — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Abstention — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Positional Bias — 1× in this paper · explained in 2 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Standardized Benchmarks — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Generating Sequences With Recurrent Neural Networks

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models