Computational Visual Cataloguing

Computational Visual Cataloguing | 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 Computational Visual Cataloguing Hussein Mohammed, Quang-Vinh Dang, Anna Kinder, Sandra Richter This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8849242/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Digitisation of handwritten heritage outpaces cataloguing. Manual workflows remain slow, automatic text recognition struggles with heterogeneous scripts and layouts, and text-only transcriptions omit visual cues such as orientation, colour and writing implements. This limits discovery and reuse in libraries, archives and museums, especially for notebooks with complex, irregular layouts. We propose Computational Visual Cataloguing (CVC), an extensible computer vision framework that performs content-bearing page detection, word localisation irrespective of script or language, and word-level visual-attribute classification (orientation, colour, writing implement). Our framework is designed to learn robustly from densely written manuscript pages and limited human annotations, reflecting the natural challenges of historical handwritten collections. These outputs populate structured, searchable catalogue fields. The contributions of this work are: (1) the formulation of CVC and a word-level visual-attribute schema for handwritten heritage; (2) task-specific methods and publicly released models for page detection, cross-script word localisation and visual-attribute classification; (3) a manually annotated dataset and full computational catalogue for Rainer Maria Rilke’s notebooks; and (4) ScriptSight, a tool for exploring and exploiting these catalogues. We evaluate CVC on Rilke’s notebooks, a culturally significant corpus with multilingual content, varied orientations and colours, diverse implements and irregular layouts. we report quantitative performance for individual components (including F1-based evaluation and ablation studies) and qualitative assessment of the integrated system, which together demonstrate its applicability to complex pages and its practical utility for exploratory cataloguing. Computer Architecture and Engineering computer vision computational cataloguing word detection historical documents Notebooks Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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-8849242","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":590259239,"identity":"0f8c5fd7-5de7-4147-994e-5930110df67d","order_by":0,"name":"Hussein Mohammed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABWUlEQVRIie2Rv2vCQBTHnwjnEsmaEEz/hZOAElr6t3hI65KAk3QUBLukdL2hQ/8Ep5NukQwOFbOenIMgODUlIBRLFXqktAlV+mMrJZ/hePeOD9/HO4CcnD8ITs9GtoNOAIqAZOUnd+0Hyvk3CuwrQXIeUupaaxnH7Tmol+5o3QZm1kv3Z4O4E5JhCdDi2ZmT4fVtgccfik2dmk7xCrTJY9OgICzbc9mMTgW560GpesVW5IYvijZNB+MOKipYjsEdbCggyMB3mSj3ZRGoT1qZBYRqPpJPqdJarrdSOeKO9ZIoYcTErj+VCiB9965sM0oDGyAVGVd7S+EypdD3E8VIUtQuMjIbmzzUdA8HSlUWxwoWFuYRm3nTppUoFbayqFbo2V6qjOXGNtvANMeOJZQLYeLQZXzTOa0MQh/pEZtXqBqM+AY+o+x9UwaNdA/2v0D1f2vk5OTk/CteAb+cksXAbEG0AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-5020-3592","institution":"The Visual Manuscript Analysis lab (VMA), The Centre for the Study of Manuscript Cultures (CSMC), Universität Hamburg","correspondingAuthor":true,"prefix":"","firstName":"Hussein","middleName":"","lastName":"Mohammed","suffix":""},{"id":590259240,"identity":"0aff5f17-623f-4eb9-aba6-d7cb7ac4b44c","order_by":1,"name":"Quang-Vinh Dang","email":"","orcid":"https://orcid.org/0000-0002-6715-7112","institution":"The Visual Manuscript Analysis lab (VMA), The Centre for the Study of Manuscript Cultures (CSMC), Universität Hamburg","correspondingAuthor":false,"prefix":"","firstName":"Quang-Vinh","middleName":"","lastName":"Dang","suffix":""},{"id":590259241,"identity":"5138fa18-f38b-4723-bc60-630c5ed8f2f0","order_by":2,"name":"Anna Kinder","email":"","orcid":"","institution":"German Literature Archive (DLA)","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Kinder","suffix":""},{"id":590259242,"identity":"28dea819-3d8f-4013-991f-4ddcbd50b3af","order_by":3,"name":"Sandra Richter","email":"","orcid":"","institution":"German Literature Archive (DLA)","correspondingAuthor":false,"prefix":"","firstName":"Sandra","middleName":"","lastName":"Richter","suffix":""}],"badges":[],"createdAt":"2026-02-11 08:43:31","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8849242/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-8849242/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102964713,"identity":"12dc5e0e-f0f7-4fe7-b44c-9128b8a7396b","added_by":"auto","created_at":"2026-02-19 04:23:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":14077438,"visible":true,"origin":"","legend":"","description":"","filename":"CVC.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8849242/v2_covered_e94506ec-b4b8-46fd-9bf9-1fcc5f5c4063.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"Computational Visual Cataloguing","fulltext":[],"fulltextSource":"","fullText":"","funders":[{"identity":"b908f756-f483-4578-845e-b95726b0180d","identifier":"10.13039/501100001659","name":"Deutsche Forschungsgemeinschaft","awardNumber":"390893796","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Germany’s Excellence Strategy - 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Manual workflows remain slow, automatic text recognition struggles with heterogeneous scripts and layouts, and text-only transcriptions omit visual cues such as orientation, colour and writing implements. This limits discovery and reuse in libraries, archives and museums, especially for notebooks with complex, irregular layouts. We propose Computational Visual Cataloguing (CVC), an extensible computer vision framework that performs content-bearing page detection, word localisation irrespective of script or language, and word-level visual-attribute classification (orientation, colour, writing implement). Our framework is designed to learn robustly from densely written manuscript pages and limited human annotations, reflecting the natural challenges of historical handwritten collections. These outputs populate structured, searchable catalogue fields. 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