ICDAR 2025 Competition on FEw-Shot Text line segmentation of ancient handwritten documents (FEST)

• URL: http://arxiv.org/abs/2509.12965v1
• Date: Tue, 16 Sep 2025 11:17:03 GMT
• Title: ICDAR 2025 Competition on FEw-Shot Text line segmentation of ancient handwritten documents (FEST)
• Authors: Silvia Zottin, Axel De Nardin, Giuseppe Branca, Claudio Piciarelli, Gian Luca Foresti,
• Abstract summary: Few-Shot Text Line of Ancient Handwritten Documents (FEST) Competition.<n>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.<n> 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.
• Score: 5.794214983347422
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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