基于自回归有序笔画实例预测的手写轨迹恢复
Handwriting Trajectory Recovery via Autoregressive Ordered Stroke Instance Prediction
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中文总结 AI 辅助
针对离线手写轨迹恢复的时间信息缺失问题，提出两阶段自回归有序笔画预测框架，在中文手写任务上优于基线方法，且具备跨语言泛化能力。
中文摘要 AI 辅助
手写轨迹恢复旨在推断隐藏在静态手写图像背后的动态书写过程。由于离线手写仅保留了最终的空间墨迹模式，笔画顺序、书写方向以及笔尖运动等时间信息均已丢失，使得轨迹恢复具有固有的模糊性。现有的基于学习的方法通常直接预测完整的字符轨迹，而未明确利用手写的笔画级组织结构。我们认为恢复书写过程应遵循书写过程本身，据此提出了一个两阶段框架：首先恢复有序笔画实例，再重建笔画内的连续运动。第一阶段通过自回归有序笔画预测整合了笔画提取与笔画顺序恢复，而与方向相关的结构线索进一步支持笔画内轨迹生成。在中文手写数据集上的实验表明，所提出的有序预测方法比事后笔画排序更有效；即使不进行轨迹简化，我们的全点模型也取得了比所有对比基线数值更优的结果，受控分析显示轨迹采样密度会显著影响测得的恢复性能。额外实验验证了该模型对未见过的中文字符类别具有泛化能力，且可跨语言扩展至英文和泰米尔语手写。
英文摘要
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.
发表机构
- Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
- School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）
- School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
机构由 AI 辅助整理，请以论文原文为准。
