On self-supervision in historical handwritten document segmentation

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dc.contributor.authorBaloun, Josef
dc.contributor.authorPrantl, Martin
dc.contributor.authorLenc, Ladislav
dc.contributor.authorMartínek, Jiří
dc.contributor.authorKrál, Pavel
dc.date.accessioned2026-02-18T10:43:30Z
dc.date.available2026-02-18T10:43:30Z
dc.date.issued2025
dc.description.abstract-translatedHistorical document analysis plays a crucial role in understanding and preserving our past. However, this task is often hindered by challenges such as limited annotated training data and the diverse nature of historical handwritten documents. In this paper,we explore the potential of self-supervised learning (SSL) in historical document analysis,with a particular focus on historical handwritten document segmentation, to overcome the need for extensive annotated data while enhancing efficiency and robustness. We present an overview of SSL methods suitable for historical document analysis and discuss their potential applications and benefits. Furthermore, we present an approach for SSL in the document domain, considering various setups, augmentations, and resolutions. We also provide experimental results that demonstrate its feasibility and effectiveness. Our findings indicate that most document segmentation tasks can be effectively addressed using SSL features, highlighting the potential of SSL to advance historical document analysis and pave the way for more efficient and robust document processing workflows.en
dc.description.sponsorshipEH23_021/0008436, SGS-2025-02cs
dc.format16 s.cs
dc.identifier.urihttp://hdl.handle.net/11025/64671
dc.language.isoenen
dc.publisherSpringeren
dc.rights© CC BY 4.0en
dc.rights.accessopenAccessen
dc.subjecthistorický ručně psaný dokumentcs
dc.subjectsamostudiumcs
dc.subjectdigitalizace dokumentůcs
dc.subjectsémantická segmentacecs
dc.subject.translatedhistorical handwritten documenten
dc.subject.translatedself-supervised learningen
dc.subject.translateddocument digitizationen
dc.subject.translatedsemantic segmentationen
dc.titleOn self-supervision in historical handwritten document segmentationen
dc.typearticleen
dc.typečlánekcs
dc.type.statusPeer revieweden
dc.type.versionpublishedVersionen
local.files.count1*
local.files.size13302218*
local.has.filesyes*

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