Tabular Context-aware Optical Character Recognition and Tabular Data Reconstruction for Historical Records

Tabular Context-aware Optical Character Recognition and Tabular Data Reconstruction for Historical Records | 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 Tabular Context-aware Optical Character Recognition and Tabular Data Reconstruction for Historical Records Loitongbam Gyanendro Singh, Stuart E Middleton This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5462018/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jul, 2025 Read the published version in International Journal on Document Analysis and Recognition (IJDAR) → Version 1 posted 13 You are reading this latest preprint version Abstract Digitizing historical tabular records is essential for preserving and analyzing valuable data across various fields, but it presents challenges due to complex layouts, mixed text types, and degraded document quality. This paper introduces a comprehensive framework to address these issues through three key contributions. First, it presents UoS_Data_Rescue, a novel dataset of 1,113 historical logbooks with over 594,000 annotated text cells, designed to handle the complexities of handwritten entries, aging artifacts, and intricate layouts. Second, it proposes a novel context-aware text extraction approach (TrOCR-ctx) to reduce cascading errors during table digitization. Third, it proposes an enhanced end-to-end OCR pipeline that integrates TrOCR-ctx with ByT5 for real-time post-OCR correction, providing improved multilingual support. This pipeline reduces errors encountered in table digitization tasks by correcting OCR outputs in real time during training. The model achieves superior performance with a 0.049 word error rate and 0.035 character error rate, outperforming existing methods by up to 41% in OCR tasks and 10.74% in table reconstruction tasks. This framework offers a robust solution for large-scale digitization of tabular documents, extending its applications beyond climate records to other domains requiring structured document preservation. The dataset and implementation are available as open-source resources. Optical Character Recognition Tabular Structure Recognition Semi-Supervised Learning Historical Document Analysis Data Annotation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 01 Jul, 2025 Read the published version in International Journal on Document Analysis and Recognition (IJDAR) → Version 1 posted Editorial decision: Revision requested 09 Feb, 2025 Reviews received at journal 03 Feb, 2025 Reviews received at journal 27 Jan, 2025 Reviewers agreed at journal 26 Jan, 2025 Reviews received at journal 25 Jan, 2025 Reviewers agreed at journal 24 Jan, 2025 Reviewers agreed at journal 19 Jan, 2025 Reviews received at journal 09 Dec, 2024 Reviewers agreed at journal 29 Nov, 2024 Reviewers invited by journal 29 Nov, 2024 Editor assigned by journal 23 Nov, 2024 Submission checks completed at journal 15 Nov, 2024 First submitted to journal 15 Nov, 2024 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. 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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-5462018","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":388181706,"identity":"05d5cda2-621f-4ae6-bd9d-b6a5b122f8e0","order_by":0,"name":"Loitongbam Gyanendro Singh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABO0lEQVRIiWNgGAWjYPACZhBuYEgAsSWA+AOSnAFuLYxALQkQLYwzIDqJ0MIA1cLMg0eLfETyswcfd1jn8zMwNj54+MMmTz66+elmmz82dfzSDYwffjAcNkbTYngjzdxw5pl0y5kNjM0GCQlpxYZ3jpndzm1Lk5Ccc4BZsofhsBm6lhkJZtK8bYcNDA4wtkkkJBxO3AgUuZ3bcFjC4EYCgzQDw2EbDC3p36T/ompJ/3bb4g9YC/NvLFrkJXLMpBmRtcwHitxmYANrYQPZgu4wA543ZZK9bekGks0gv6SlJW6QOVN2s7ctTXLmjMQ2yx6DdHTvy7enb5P42WZtwM/efBAYYDaJ82e3b7vx448NP79E8uEbPyqsDRvQbLmQgBQ1YJEDcElQTGFGpHz/AXSRBgxFo2AUjIJRMMIBAF1vdLDPAFXeAAAAAElFTkSuQmCC","orcid":"","institution":"University of Southampton","correspondingAuthor":true,"prefix":"","firstName":"Loitongbam","middleName":"Gyanendro","lastName":"Singh","suffix":""},{"id":388181707,"identity":"11952d17-8032-4bf4-9ed5-7e29b512d2d3","order_by":1,"name":"Stuart E Middleton","email":"","orcid":"","institution":"University of Southampton","correspondingAuthor":false,"prefix":"","firstName":"Stuart","middleName":"E","lastName":"Middleton","suffix":""}],"badges":[],"createdAt":"2024-11-15 16:53:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5462018/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5462018/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10032-025-00543-9","type":"published","date":"2025-07-01T15:57:40+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86180207,"identity":"12e5400c-a66c-4be1-af67-473e77af1ba8","added_by":"auto","created_at":"2025-07-07 16:21:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1169957,"visible":true,"origin":"","legend":"","description":"","filename":"OCRFinalICDAR2025v2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5462018/v1_covered_cf5ed51b-ebbe-4b8d-ade4-d6c54853c33d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Tabular Context-aware Optical Character Recognition and Tabular Data Reconstruction for Historical Records","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-on-document-analysis-and-recognition-ijdar","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijda","sideBox":"Learn more about [International Journal on Document Analysis and Recognition (IJDAR)](http://link.springer.com/journal/10032)","snPcode":"10032","submissionUrl":"https://submission.nature.com/new-submission/10032/3","title":"International Journal on Document Analysis and Recognition (IJDAR)","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Optical Character Recognition, Tabular Structure Recognition, Semi-Supervised Learning, Historical Document Analysis, Data Annotation","lastPublishedDoi":"10.21203/rs.3.rs-5462018/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5462018/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":" Digitizing historical tabular records is essential for preserving and analyzing valuable data across various fields, but it presents challenges due to complex layouts, mixed text types, and degraded document quality. 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