Handwriting Trajectory Recovery via Autoregressive Ordered Stroke Instance Prediction

The paper presents a two‑stage method for recovering handwriting trajectories from static images. First, it predicts ordered stroke instances autoregressively, then reconstructs continuous motion within each stroke using direction‑related cues. Experiments on Chinese, English, and Tamil handwriting show that this ordered prediction outperforms post‑hoc ordering and baseline models, and that sampling density significantly impacts performance.

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