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Developing robust computational methods is hindered by the scarcity of high-quality ground truth annotations, which require expert knowledge and are time-intensive to produce. Few-shot learning has emerged as a promising solution by enabling model training with minimal annotated data, yet its application to historical document analysis is still largely unexplored. To address this limitation, we introduce U-DIADS-TL (Uniud - Document Image Analysis DataSet - Text Line), a dataset specifically designed for text line segmentation in ancient manuscripts. U-DIADS-TL provides noise-free annotations with non-overlapping text elements and accommodates diverse document structures, including multi-column layouts. To encourage few-shot learning approaches, we offer only three training images, allowing researchers to develop segmentation models that can generalize from limited supervision. Our dataset serves as a critical bridge between deep learning and historical document analysis, fostering the creation of efficient, adaptable segmentation models for real-world applications.<\/jats:p>","DOI":"10.1007\/s10032-026-00585-7","type":"journal-article","created":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T02:51:19Z","timestamp":1776480679000},"page":"647-658","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["U-DIADS-TL: a novel dataset for text line segmentation in historical manuscripts"],"prefix":"10.1007","volume":"29","author":[{"given":"Silvia","family":"Zottin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Axel","family":"De Nardin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claudio","family":"Piciarelli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gian Luca","family":"Foresti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,18]]},"reference":[{"key":"585_CR1","doi-asserted-by":"publisher","unstructured":"De\u00a0Nardin, A., Zottin, S., Paier, M., Foresti, G.L., Colombi, E., Piciarelli, C.: Efficient few-shot learning for pixel-precise handwritten document layout analysis. 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