CASHG: Context-Aware Stylized Online Handwriting Generation
Jinsu Shin, Sungeun Hong, JinYeong Bak

TL;DR
CASHG is a novel context-aware handwriting generator that explicitly models character connectivity and spacing to produce natural, style-consistent sentence-level online handwriting trajectories.
Contribution
It introduces a character context encoder and a bigram-aware Transformer decoder with a curriculum training strategy for improved sentence-level handwriting synthesis.
Findings
CASHG outperforms existing methods in connectivity and spacing metrics.
It maintains competitive trajectory similarity scores.
Human evaluations favor CASHG's naturalness and style consistency.
Abstract
Online handwriting represents strokes as time-ordered trajectories, which makes handwritten content easier to transform and reuse in a wide range of applications. However, generating natural sentence-level online handwriting that faithfully reflects a writer's style remains challenging, since sentence synthesis demands context-dependent characters with stroke continuity and spacing. Prior methods treat these boundary properties as implicit outcomes of sequence modeling, which becomes unreliable at the sentence scale and under limited compositional diversity. We propose CASHG, a context-aware stylized online handwriting generator that explicitly models inter-character connectivity for style-consistent sentence-level trajectory synthesis. CASHG uses a Character Context Encoder to obtain character identity and sentence-dependent context memory and fuses them in a bigram-aware…
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