DARE: A large-scale handwritten DAte REcognition system

DARE: A large-scale handwritten DAte REcognition system | 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 DARE: A large-scale handwritten DAte REcognition system Christian Møller Dahl, Torben Skov Dyg Johansen, Emil Nørmark Sørensen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6412887/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Handwritten text recognition for historical documents is an important task, but it remains challenging due to insufficient training data combined with wide variability in writing styles and degradation of historical documents. In the context of recognizing handwritten dates, we propose a model based on the EfficientNetV2 architecture. The model is characterized by its fast training speed, robust-ness to parameter choices, and accurate transcription of handwritten dates from various sources. For our training process, we build and introduce a database containing nearly 10 million tokens derived from over 2.2 million images of handwritten dates, extracted and segmented from diverse historical documents. Considering that dates are among the most prevalent pieces of information in historical documents, and given the existence of millions of such documents in historical archives, achieving efficient and automated transcription of dates holds the potential for substantial cost savings compared to manual transcription efforts. We demonstrate that training on handwritten text that exhibits substantial variability in writing styles yields robust models for recognizing general handwritten text and that transfer learning from the DARE system increases transcription accuracy substantially, allowing one to obtain high accuracy even when using relatively small training samples on entirely new types of documents. The DARE database is freely available at https://www.kaggle.com/datasets/sdusimonwittrock/dare-database . Code to be made available at https://github.com/TorbenSDJohansen/DARE . Handwritten text recognition Dataset Transfer learning Handwritten dates Foundation model Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 Jul, 2025 Reviews received at journal 30 May, 2025 Reviews received at journal 27 May, 2025 Reviewers agreed at journal 06 May, 2025 Reviewers agreed at journal 05 May, 2025 Reviewers invited by journal 04 May, 2025 Editor assigned by journal 21 Apr, 2025 Submission checks completed at journal 16 Apr, 2025 First submitted to journal 09 Apr, 2025 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. 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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-6412887","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":481269220,"identity":"c6f46367-53ab-4778-b2e7-7c1b6222c63d","order_by":0,"name":"Christian Møller Dahl","email":"","orcid":"","institution":"University of Southern Denmark","correspondingAuthor":false,"prefix":"","firstName":"Christian","middleName":"Møller","lastName":"Dahl","suffix":""},{"id":481269221,"identity":"6cdcd561-e64c-4c7b-929b-80f28624fee7","order_by":1,"name":"Torben Skov Dyg 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