HADOCR: A Hierarchical Attention-Driven Framework with Dynamic Sampling for Ancient Chinese Medical Literature Recognition

HADOCR: A Hierarchical Attention-Driven Framework with Dynamic Sampling for Ancient Chinese Medical Literature Recognition | 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 HADOCR: A Hierarchical Attention-Driven Framework with Dynamic Sampling for Ancient Chinese Medical Literature Recognition Jia He This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6036640/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 ancient Chinese medical literature faces significant challenges due to its complex typefaces, irregular layouts, degraded documents, and varied writing styles across dynasties. This research addresses the limitations of existing scene text recognition (STR) methods, which perform poorly on historical texts, especially medical manuscripts. The primary research question is how to design a robust and effective method for ancient Chinese medical text recognition.We propose HADOCR, a novel end-to-end framework for ancient Chinese medical text recognition. The model leverages a Vision Transformer (ViT) for visual feature extraction, followed by a Feature Fusion Block (FFB) to mix local and global features. Two innovative modules are introduced: Dynamic Ratio Sampling (DRS), which adapts to multi-scale sampling while preserving aspect ratios and structural features, and Dual-Attention Feature Rearrangement (DAFR), which applies hierarchical attention to improve handling of character deformation and irregular text arrangements.We propose HADOCR, a novel end-to-end framework for ancient Chinese medical text recognition. The model leverages a Vision Transformer (ViT) for visual feature extraction, followed by a Feature Fusion Block (FFB) to mix local and global features. Two innovative modules are introduced: Dynamic Ratio Sampling (DRS), which adapts to multi-scale sampling while preserving aspect ratios and structural features, and Dual-Attention Feature Rearrangement (DAFR), which applies hierarchical attention to improve handling of character deformation and irregular text arrangements.HADOCR demonstrates superior performance in ancient Chinese medical text recognition, significantly improving feature preservation and handling spatial deformations. The introduction of the ACML dataset, containing over 100,000 instances, provides a valuable resource for future research in historical document recognition. Our work paves the way for better digital preservation and knowledge mining of traditional Chinese medical literature and has broader applications in historical document recognition. Ancient Chinese literature Scene text recognition Dynamic Ratio Sampling Dual-Attention Feature Rearrangement. 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-6036640","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":417208274,"identity":"dbaac4a1-8e45-40c5-905e-615e21d78492","order_by":0,"name":"Jia He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIiWNgGAWjYBACAwaGhAMMBRIQ3scGEMnYeICwFgOIFsaZDQxAFmMDIS1wkoGZF6yFgQGvFnP2hocHfhhYyJvzrzH8bLvDpk63/TDQlhqbaFxaLHsOJBzsMZAw3DnjjbF07pk0CbMziUAtx9JyG3A57EZCwgEeAwnGDTfOGEjnth2WMDsA1MLYcBi3lvsPEg7+MZCwB2ox/m0J0nL+IQEtNxgSDgNtSdxwvsdMmhGk5QYhW84kJByWMZBI3nCDrcyyty1NctsNoC0J+Pxy/EzyxzcVdbYbzh/efONnmw2/2fn0hw8+1Njg1MLAwJMAoSUyDBCCCTiVgwD7AQjNf/wBXnWjYBSMglEwcgEAZshq0AfPJYcAAAAASUVORK5CYII=","orcid":"","institution":"Changzhou Municipal Hospital of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Jia","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2025-02-15 12:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6036640/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6036640/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79043131,"identity":"d7bff8e6-0c5b-476c-aaee-c21b0feddab6","added_by":"auto","created_at":"2025-03-23 12:53:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4671479,"visible":true,"origin":"","legend":"","description":"","filename":"HADOCRAHierarchicalAttentionDrivenFrameworkwithDynamicSamplingforAncientChineseMedicalLiteratureRecognition1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6036640/v1_covered_a21e6280-ce41-4fcd-aa5f-c419dde98eb2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"HADOCR: A Hierarchical Attention-Driven Framework with Dynamic Sampling for Ancient Chinese Medical Literature Recognition","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":"Ancient Chinese literature, Scene text recognition, Dynamic Ratio Sampling, Dual-Attention Feature Rearrangement.","lastPublishedDoi":"10.21203/rs.3.rs-6036640/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6036640/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe digitization of ancient Chinese medical literature faces significant challenges due to its complex typefaces, irregular layouts, degraded documents, and varied writing styles across dynasties. 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