| title | BoundInk: Boundary-Aware Online Handwriting Generation |
|---|---|
| authors | Jinsu Shin, Sungeun Hong, JinYeong Bak |
| date | 2026-09-20 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2604.02103 |
| affiliation | Sungkyunkwan University |
| upvotes | 6 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Realistic online handwriting depends not only on individual character shapes, but also on how a writer connects, spaces, and aligns adjacent characters. Existing methods rely primarily on long-range sequence modeling and capture these inter-character behaviors only implicitly. This often produces plausible glyphs accompanied by broken cursive joins, inconsistent spacing, or writer-inconsistent transitions. We introduce BoundInk, a writer-conditioned framework that treats inter-character boundaries as explicit generation units. By jointly modeling local transitions and surrounding text context, BoundInk preserves writer-specific glyph appearance while improving connectivity and spacing across complete text lines. We further introduce a boundary-aware evaluation framework that directly assesses cursive continuity and spatial relationships between characters beyond conventional trajectory similarity. Across three benchmark-matched settings, BoundInk improves all applicable boundary-quality measures and reduces normalized dynamic time warping by 17.6--47.8%. In blind human evaluations, BoundInk outputs are preferred in 78.0--82.6% of valid criterion-wise judgments. |
cite
@misc{shin2026boundink,
title={BoundInk: Boundary-Aware Online Handwriting Generation},
author={Jinsu Shin and Sungeun Hong and JinYeong Bak},
year={2026},
eprint={2604.02103},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2604.02103}
}