Handwriting Trajectory Recovery via Autoregressive Ordered Stroke Instance Prediction

2026-09-02

AI Summary

研究通过自回归有序笔画实例预测方法解决静态手写图像中书写轨迹恢复问题，分为提取有序笔画和重建连续笔迹两阶段。

Abstract

Handwriting trajectory recovery aims to infer the dynamic writing process hidden behind a static handwritten image. Since offline handwriting preserves only the final spatial ink pattern, temporal information such as stroke order, writing direction, and pen-tip motion is lost, making recovery inherently ambiguous. Existing learning-based methods often directly predict the complete character trajectory without explicitly exploiting the stroke-level organization of handwriting. We argue that recovering the writing process should follow the writing process itself. Accordingly, we propose a two-stage framework that first recovers ordered stroke instances and then reconstructs continuous within-stroke motion. The first stage integrates stroke extraction and stroke-order recovery through autoregressive ordered stroke prediction, while direction-related structural cues further support within-stroke trajectory generation. Experiments on Chinese handwriting show that the proposed ordered prediction is more effective than post-hoc stroke ordering. Even without trajectory simplification, our full-point model achieves numerically better results than those reported by all compared baselines, while a controlled analysis shows that trajectory sampling density substantially affects measured recovery performance. Additional experiments demonstrate generalization to unseen Chinese character categories and cross-language extensibility to English and Tamil handwriting.

Problem

• handwriting trajectory recovery
• stroke order
• temporal information

Innovation

• autoregressive ordered stroke prediction
• two-stage framework
• handwriting trajectory recovery
• stroke-level organization