Abstract
This paper investigates various factors that influence the performance of end-to-end deep learning approaches for historical writer identification (HWI), a task that remains challenging due to the diversity of handwriting styles, document degradation, and the limited number of labelled samples per writer. These conditions often make accurate recognition difficult, even for human experts. Traditional HWI methods typically rely on handcrafted image processing and clustering techniques, which tend to perform well on small and carefully curated datasets. In contrast, end-to-end pipelines aim to automate the process by learning features directly from document images. However, our experiments show that many of these models struggle to generalise in more realistic, document-level settings, especially under zero-shot scenarios where writers in the test set are not present in the training data. We explore different combinations of pre-processing methods, backbone architectures, and post-processing strategies, including text segmentation, patch sampling, and feature aggregation. The results suggest that most configurations perform poorly due to weak capture of low-level visual features, inconsistent patch representations, and high sensitivity to content noise. Still, we identify one end-to-end setup that achieves results comparable to the top-performing system, despite using a simpler design. These findings point to key challenges in building robust end-to-end systems and offer insight into design choices that improve performance in historical document writer identification.
Abstract (translated)
本文研究了影响端到端深度学习方法在历史书写者识别（HWI）任务中的各种因素。由于手写风格的多样性、文档退化以及每位书写者的标注样本数量有限，这一任务仍然具有挑战性，甚至人类专家也难以准确地进行识别。传统的历史书写者识别方法通常依赖于手工制作的图像处理和聚类技术，在经过精心策划的小型数据集上表现出色。相比之下，端到端流程旨在通过直接从文档图像中学习特征来自动化整个过程。然而，我们的实验表明，许多这些模型在更加现实、以文档为单位的情境下难以泛化，特别是在零样本场景（测试集中出现的书写者不在训练数据中）中表现尤其不佳。 本文探讨了不同的预处理方法、主干架构和后处理策略组合的效果，包括文本分割、补丁采样以及特征聚合。结果显示，大多数配置由于对低级视觉特征捕捉能力弱、补丁表示不一致以及内容噪声敏感度高而表现较差。尽管如此，我们发现有一种端到端设置在使用更为简单的设计时仍能取得与最佳系统相媲美的结果。 这些发现揭示了构建稳健的端到端系统的挑战，并为改善历史文档书写者识别中的性能提供了关于设计选择的见解。
URL
https://arxiv.org/abs/2510.18671
