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• CASHG explicitly models inter-character connectivity for style-consistent sentence-level trajectory synthesis, addressing limitations of prior methods that treat boundary properties as implicit outcomes.method 90%
• CASHG uses a Character Context Encoder to obtain character identity and sentence-dependent context memory.method 90%
• CASHG employs a bigram-aware sliding-window Transformer decoder that emphasizes local predecessor-current transitions.technical 90%
• CASHG training proceeds through a three-stage curriculum from isolated glyphs to full sentences, improving robustness under sparse transition coverage.method 90%
• The paper introduces Connectivity and Spacing Metrics (CSM), a boundary-aware evaluation suite that quantifies cursive connectivity and spacing similarity.method 90%
• Under benchmark-matched evaluation protocols, CASHG consistently improves CSM over comparison methods while remaining competitive in DTW-based trajectory similarity.result 85%
• Prior methods treating boundary properties as implicit outcomes of sequence modeling become unreliable at the sentence scale and under limited compositional diversity.limitation 80%
• Generating natural sentence-level online handwriting that faithfully reflects a writer's style remains challenging due to demands for context-dependent characters with stroke continuity and spacing.technical 75%

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