Word Segmentation using Self-Supervised Hierarchical Transformers for Scriptio Continua in Greek and Latin

Word Segmentation using Self-Supervised Hierarchical Transformers for Scriptio Continua in Greek and Latin | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Word Segmentation using Self-Supervised Hierarchical Transformers for Scriptio Continua in Greek and Latin Sophie Robert-Hayek, Victoria Eyharabide This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8107485/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The digitization of historical documents written in Scriptio Continua, a style of writing without spaces between words, requires not only accurate Optical Character Recognition (OCR), but also a second step of very costly manual segmentation to convert character sequences into meaningful words. This article presents a new self-supervised hierarchical transformer model that automates word segmentation for Scriptio Continua in Greek and Latin texts. Our method utilizes readily available printed editions to train, first, a character-level transformer for word boundary labeling and, second, a word-level transformer to reclassify segmentation candidates based on a perplexity score. Evaluated on a diverse corpus of Greek and Latin codices (4th-6th centuries) and Byzantine seals (6th-12th centuries), our approach significantly outperforms state-of-the-art Bayesian (NHPYLM) and flat neural network models, achieving F1 scores of 96%, 94%, and 42% for boundary detection, respectively, on the three considered datasets. While significantly improving the performance of current approaches on highly abbreviated texts, this study highlights ongoing challenges for the study of abbreviations on smaller epigraphic objects, such as seals and coins. We additionally provide our code and datasets to ensure full reproducibility and foster future research. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8107485","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":553368286,"identity":"35b20d2a-6c16-4da8-842b-994f362f234c","order_by":0,"name":"Sophie Robert-Hayek","email":"data:image/png;base64,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","orcid":"","institution":"Sorbonne University","correspondingAuthor":true,"prefix":"","firstName":"Sophie","middleName":"","lastName":"Robert-Hayek","suffix":""},{"id":553368289,"identity":"95815d6f-cf8f-4092-b5f4-a4727ce17aee","order_by":1,"name":"Victoria Eyharabide","email":"","orcid":"","institution":"Sorbonne University","correspondingAuthor":false,"prefix":"","firstName":"Victoria","middleName":"","lastName":"Eyharabide","suffix":""}],"badges":[],"createdAt":"2025-11-13 15:53:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8107485/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8107485/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97407923,"identity":"093560d3-aeb6-4f46-8d7f-9937ec3a5734","added_by":"auto","created_at":"2025-12-04 04:24:37","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4386,"visible":true,"origin":"","legend":"","description":"","filename":"d9ca436b52cd42a9b16a0386ba18b13e.json","url":"https://assets-eu.researchsquare.com/files/rs-8107485/v1/a99f7b82532a5d937a7d604d.json"},{"id":102748521,"identity":"34f0729c-eee7-4549-9718-ce05ba90fa71","added_by":"auto","created_at":"2026-02-16 09:11:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":711494,"visible":true,"origin":"","legend":"","description":"","filename":"ICDARJournal2025final.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8107485/v1_covered_027cad85-70e5-4006-856d-29c20f603109.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Word Segmentation using Self-Supervised Hierarchical Transformers for Scriptio Continua in Greek and Latin","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8107485/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8107485/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The digitization of historical documents written in Scriptio Continua, a style of writing without spaces between words, requires not only accurate Optical Character Recognition (OCR), but also a second step of very costly manual segmentation to convert character sequences into meaningful words. 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