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 sliding-window Transformer decoder that emphasizes local predecessor--current transitions, complemented by gated context fusion for sentence-level this http URL proceeds through a three-stage curriculum from isolated glyphs to full sentences, improving robustness under sparse transition coverage. We further introduce Connectivity and Spacing Metrics (CSM), a boundary-aware evaluation suite that quantifies cursive connectivity and spacing similarity. Under benchmark-matched evaluation protocols, CASHG consistently improves CSM over comparison methods while remaining competitive in DTW-based trajectory similarity, with gains corroborated by a human evaluation.
Abstract (translated)
在线手写将笔迹表示为时间有序的轨迹，这使得手写内容在众多应用中更易于转换和复用。然而，生成能忠实反映书写者风格的、自然的句子级在线手写仍具挑战性，因为句子合成需要具备笔划连贯性和间距的、依赖上下文的字符。先前方法将这些边界特性视为序列建模的隐式结果，这在句子尺度上及组合多样性有限时变得不可靠。我们提出了CASHG，一个上下文感知的、风格化的在线手写生成器，它通过显式建模字符间连接性，来实现风格一致的句子级轨迹合成。CASHG使用一个字符上下文编码器来获取字符身份和句子依赖的上下文记忆，并在一个关注二元组的滑动窗口Transformer解码器中融合它们，该解码器强调局部前字-当前字过渡，并辅以门控上下文融合以实现句子级一致性。训练通过一个从独立字形到完整句子的三阶段渐进式方案进行，提高了稀疏过渡覆盖下的鲁棒性。我们还引入了连通与间距度量（CSM），一个边界感知的评估套件，用于量化草书连接性和间距相似性。在匹配基准的评估协议下，CASHG在CSM上持续优于对比方法，同时在基于动态时间规整的轨迹相似度上保持竞争力，这些提升也通过人工评估得到了证实。
URL
https://arxiv.org/abs/2604.02103
