Abstract
Text line segmentation is a critical step in handwritten document image analysis. Segmenting text lines in historical handwritten documents, however, presents unique challenges due to irregular handwriting, faded ink, and complex layouts with overlapping lines and non-linear text flow. Furthermore, the scarcity of large annotated datasets renders fully supervised learning approaches impractical for such materials. To address these challenges, we introduce the Few-Shot Text Line Segmentation of Ancient Handwritten Documents (FEST) Competition. Participants are tasked with developing systems capable of segmenting text lines in U-DIADS-TL dataset, using only three annotated images per manuscript for training. The competition dataset features a diverse collection of ancient manuscripts exhibiting a wide range of layouts, degradation levels, and non-standard formatting, closely reflecting real-world conditions. By emphasizing few-shot learning, FEST competition aims to promote the development of robust and adaptable methods that can be employed by humanities scholars with minimal manual annotation effort, thus fostering broader adoption of automated document analysis tools in historical research.
Abstract (translated)
文本行分割是手写文档图像分析中的关键步骤。然而，在历史手写文档中进行文本行分割面临着一些独特的挑战，例如不规则的笔迹、褪色的墨水以及包含重叠线条和非线性文本流动的复杂布局。此外，由于大型标注数据集的稀缺，完全监督的学习方法在这种材料上显得不太实际。为了解决这些问题，我们推出了《古代手写文档文本行分割少样本学习竞赛》（FEST）。参赛者需要开发能够在使用U-DIADS-TL数据集中仅有的三张标记图像进行训练的情况下，对文本行进行分割的系统。 该竞赛的数据集包含了一系列具有不同布局、退化程度和非标准格式的古代手稿，这与真实世界的情况非常接近。通过强调少样本学习，FEST竞赛旨在促进开发出稳健且适应性强的方法，这些方法可以在需要最少人工标注努力的情况下被人文学者采用，并推动自动化文档分析工具在历史研究中的广泛应用。
URL
https://arxiv.org/abs/2509.12965
