用合成数据训练的模型，零样本识别手稿笔迹性别与作者
TextileNet: Towards Zero-shot Text-style Segmentation of Manuscripts
- 全卷积网络仅用合成数据生成像素级纹理嵌入
- 在80组对比题中人类与模型表现接近，验证有效性
- 揭示手写性别判断需谨慎，适合历史文献分析者
近年来自动作者识别系统发展迅速，但在档案古文字学领域仍受限于标注数据稀缺、开放的抄写员数据集缺失以及图像质量退化。我们提出TextileNet，一种仅基于合成数据训练的全卷积多任务网络，生成密集像素级纹理嵌入，并实现零样本迁移至历史手稿分析。作为评估方法的原创贡献，我们设计了一项包含80组配对与三元组问题的古文字视觉测验，对从普通参与者到资深古文字学者进行匿名测试，首次建立了晚期中世纪文本书写风格辨识的人类基准。利用TextileNet嵌入，我们在子词粒度上实现零样本检索，用于手写特征与性别识别。实验结果增强了TextileNet在古文字学领域的可信度，更重要的是，从实证角度表明手写性别判断需持审慎态度。
原文摘要 · Abstract (English)
Automatic writer identification systems have progressed remarkably in recent years, yet their deployment in archival paleography remains limited by the scarcity of labeled training data, open scribe sets, and degraded image quality. We present TextileNet, a fully convolutional multi-task network trained exclusively on synthetic data to produce dense pixel-level texture embeddings, which we transfer zeroshot to historical manuscript analysis. As an original contribution to evaluation methodology, we designed a paleographic visual quiz of 80 pair and triplet questions and administered it to a range from lay participants to senior paleographers under strict anonymity, establishing to our knowledge for the first time a human baseline for script-style discrimination on late medieval text. We employ TextileNet embeddings to perform zero-shot retrieval on sub-word granularity for hand and gender identification. Our experimental results help in building the credibility of TextileNet in the paleographic domain, but more than that demonstrate in experimental terms that the question of gender in handwriting needs to be treated with caution.
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