ICDAR 2025 Competition on FEw-Shot Text line segmentation of ancient handwritten documents (FEST)
This addresses the problem of limited annotated data for humanities scholars analyzing historical documents, though it is incremental as it builds on existing few-shot learning approaches.
The paper introduces the FEST competition to tackle text line segmentation in ancient handwritten documents with irregular layouts and degradation, using only three annotated images per manuscript for training to address data scarcity. The competition aims to develop robust few-shot learning methods for the U-DIADS-TL dataset, promoting automated tools for historical research.
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.