System for Detection and Recognition of Historical Arabic Manuscripts

System for Detection and Recognition of Historical Arabic Manuscripts | 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 System for Detection and Recognition of Historical Arabic Manuscripts Abrar A.Alqahtani, Samiah S.Alfahmi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5936450/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 Optical Character Recognition (OCR) technology automates the extraction and recognition of text from scanned documents or images, leveraging machine learn- ing models trained on standardized datasets. Historical Arabic manuscripts, housed in national libraries and archives around the world, hold immense cultural, religious, and historical significance. However, manual analysis is time-consuming and complex due to the cursive nature of Arabic script, context-dependent char- acter shapes, and document degradation over time. This research aims to develop a robust OCR system for detecting and recognizing text in historical Arabic manuscripts. By training machine learning models on curated datasets, the system will produce accurate digitized text, enabling historians and archaeologists to analyze content efficiently. The deployment and evaluation of the system in real world scenarios will support cultural preservation and enhance historical understanding. Artificial Intelligence and Machine Learning OCR Arabic Text Manuscripts Detection Recognition Full Text Additional Declarations The authors declare no competing interests. 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-5936450","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":409554412,"identity":"2bed71f2-7c4e-49f7-b2d8-7a47a719a3d4","order_by":0,"name":"Abrar A.Alqahtani","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0004-2456-5871","institution":"King Fahd University of Petroleum and Minerals","correspondingAuthor":true,"prefix":"","firstName":"Abrar","middleName":"","lastName":"A.Alqahtani","suffix":""},{"id":409554413,"identity":"6c925a9f-9827-40a8-b655-e54ee6aca5b8","order_by":1,"name":"Samiah S.Alfahmi","email":"","orcid":"","institution":"King Fahd University of Petroleum and Minerals","correspondingAuthor":false,"prefix":"","firstName":"Samiah","middleName":"","lastName":"S.Alfahmi","suffix":""}],"badges":[],"createdAt":"2025-01-31 12:56:44","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5936450/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5936450/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75281255,"identity":"7416ff9f-ec11-4e41-8f82-7e61c02fbd9e","added_by":"auto","created_at":"2025-02-03 02:50:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2239990,"visible":true,"origin":"","legend":"","description":"","filename":"PRABRARSAMIA.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5936450/v1_covered_5693aa64-b2f5-4875-8bc7-670ba663c4a0.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eSystem for Detection and Recognition of Historical Arabic Manuscripts\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"OCR, Arabic, Text, Manuscripts, Detection, Recognition","lastPublishedDoi":"10.21203/rs.3.rs-5936450/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5936450/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eOptical Character Recognition (OCR) technology automates the extraction and recognition of text from scanned documents or images, leveraging machine learn- ing models trained on standardized datasets. 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