发表机构
University of Graz; Università degli Studi di Napoli Federico II（格拉茨大学; 那不勒斯费德里科二世大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对自动书写者识别系统在档案古文字学应用受限的问题，提出TextileNet，利用合成数据训练生成纹理嵌入并零样本转移至手稿分析，设计测验建立人类基线，通过实验验证其可信度及手写性别问题需谨慎对待。
AI 中文摘要
近年来，自动书写者识别系统取得了显著进展，但其在档案古文字学中的应用仍受限于标记训练数据的稀缺、开放抄写集以及图像质量的下降。我们提出了TextileNet，这是一个完全卷积多任务网络，仅在合成数据上训练以生成密集的像素级纹理嵌入，并将其零样本转移到历史手稿分析中。作为评估方法的原创贡献，我们设计了一个包含80个对和三元组问题的古文字视觉测验，并在严格匿名的情况下对从普通参与者到高级古文字学家进行了测试，首次建立了中世纪晚期文本脚本风格歧视的人类基线。我们使用TextileNet嵌入对手部和性别识别进行子词粒度的零样本检索。我们的实验结果有助于建立TextileNet在古文字领域的可信度，同时从实验角度表明，手写中的性别问题需要谨慎对待。
英文摘要
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.
Commentsaccepted for publication in the ICDAR 2026 workshop (peer reviewed) "IWCP: 4th International Workshop on Computational Paleography"
