LexiAlign: A Diffusion Model Text Alignment and Refinement Method Based on Local Regeneration

LexiAlign: A Diffusion Model Text Alignment and Refinement Method Based on Local Regeneration | 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 LexiAlign: A Diffusion Model Text Alignment and Refinement Method Based on Local Regeneration Weijia Zhu, Xinjin Li, Jing Pu, Jing Tan, Minglu Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7389501/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Feb, 2026 Read the published version in The Visual Computer → Version 1 posted 11 You are reading this latest preprint version Abstract Despite the recent advances of diffusion models in high-quality image synthesis, generating visually accurate textual content remains a persistent bottleneck—commonly manifesting as misspellings, glyph distortions, and semantic drift. We introduce \textbf{LexiAlign}, a \emph{language-guided local diffusion refinement} framework that directly targets these issues through three tightly coupled components: robust \emph{optical character recognition} (OCR)-based text extraction, \emph{language model}-driven semantic correction, and high-fidelity local inpainting via masked diffusion. Unlike prior approaches that retrain large diffusion backbones or overwrite entire regions, LexiAlign performs \emph{character-level targeted repair} while preserving surrounding visual context and style. To support systematic training and evaluation, we construct \textbf{SynOCRText}, a 120k-sample benchmark covering 8 languages, over 20 fonts, diverse layouts, and fine-grained error masks. On SynOCRText, LexiAlign achieves \textbf{88.4% OCR accuracy} (+6.3% over the best baseline), Contrastive Language-Image Pretraining (CLIP) Score of 0.852 (+0.023), Peak Signal-to-Noise Ratio (PSNR) of 30.92,dB (+2.51,dB), and Structural Similarity Index Measure (SSIM) of 0.893 (+0.020). These results establish LexiAlign as a \emph{plug-and-play, domain-agnostic} solution for reliable visual text alignment, offering both \emph{quantitative superiority} and \emph{practical deployability} for creative design, advertising, and multimodal content generation. Visual Text Alignment Diffusion Models OCR-guided Inpainting Language Model Correction Multimodal Refinement Text Fidelity Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 01 Feb, 2026 Read the published version in The Visual Computer → Version 1 posted Editorial decision: Revision requested 25 Sep, 2025 Reviews received at journal 08 Sep, 2025 Reviews received at journal 01 Sep, 2025 Reviews received at journal 30 Aug, 2025 Reviewers agreed at journal 29 Aug, 2025 Reviewers agreed at journal 28 Aug, 2025 Reviewers agreed at journal 28 Aug, 2025 Reviewers invited by journal 28 Aug, 2025 Editor assigned by journal 20 Aug, 2025 Submission checks completed at journal 18 Aug, 2025 First submitted to journal 16 Aug, 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. 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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-7389501","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":508581702,"identity":"52885b53-4d2f-47e1-9cbd-49b1a2bd2891","order_by":0,"name":"Weijia Zhu","email":"","orcid":"","institution":"Zhejiang University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Weijia","middleName":"","lastName":"Zhu","suffix":""},{"id":508581703,"identity":"4a239f4c-b14f-457c-a190-403a4e0b99c8","order_by":1,"name":"Xinjin Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYBACPmYgkcDAxsPA3sDA8ADIZgMJ8+DRwgbXwnMAxCBGC5wlkQDRwkBQCzsDm8TDHXwy5pKPHz5IbNuWx8d+gPHB2za8DmM2SDzDxmM5O83YILHtdjEbTwKz4Vz8WhiBhrPxGNzOYZMAaklsA3pHmhe/FoYDYC03z0C18D9g/01AC9SWGzxQLRIJbMz4tTA2G4C1nAH6JeEc0C8SD5sl55zDrYWf//AxyZ9tx+wNjh9++OBD2e08+f7kgx/elOHWwsDA2AAkjmGIEAQ1xCgaBaNgFIyCkQoA+KpHpza0Fr4AAAAASUVORK5CYII=","orcid":"","institution":"Columbia University","correspondingAuthor":true,"prefix":"","firstName":"Xinjin","middleName":"","lastName":"Li","suffix":""},{"id":508581704,"identity":"3c27b3bb-05e1-4d6e-9be1-14d5a64fa307","order_by":2,"name":"Jing Pu","email":"","orcid":"","institution":"Sichuan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Pu","suffix":""},{"id":508581705,"identity":"59bfe91f-edf8-482a-8448-17882daed5c8","order_by":3,"name":"Jing Tan","email":"","orcid":"","institution":"Mianyang Teachers' College","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Tan","suffix":""},{"id":508581709,"identity":"758b696c-4ae2-44df-8f5c-a5c8bef6d909","order_by":4,"name":"Minglu Wang","email":"","orcid":"","institution":"Qilu University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Minglu","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-08-16 21:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7389501/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7389501/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00371-025-04345-2","type":"published","date":"2026-02-01T15:59:10+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":101692259,"identity":"53f71750-558d-4827-a8e0-97d93af30df4","added_by":"auto","created_at":"2026-02-02 16:17:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":298493,"visible":true,"origin":"","legend":"","description":"","filename":"LexiAlignLanguageGuidedLocalDiffusionRefinementforVisualTextAlignmentandFidelity.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7389501/v1_covered_31aa670b-92e9-41dd-bd28-d850da3eb11d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"LexiAlign: A Diffusion Model Text Alignment and Refinement Method Based on Local Regeneration","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":"the-visual-computer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"tvcj","sideBox":"Learn more about [The Visual Computer](http://link.springer.com/journal/371)","snPcode":"371","submissionUrl":"https://submission.nature.com/new-submission/371/3","title":"The Visual Computer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Visual Text Alignment, Diffusion Models, OCR-guided Inpainting, Language Model Correction, Multimodal Refinement, Text Fidelity","lastPublishedDoi":"10.21203/rs.3.rs-7389501/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7389501/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Despite the recent advances of diffusion models in high-quality image synthesis, generating visually accurate textual content remains a persistent bottleneck—commonly manifesting as misspellings, glyph distortions, and semantic drift. We introduce \\textbf{LexiAlign}, a \\emph{language-guided local diffusion refinement} framework that directly targets these issues through three tightly coupled components: robust \\emph{optical character recognition} (OCR)-based text extraction, \\emph{language model}-driven semantic correction, and high-fidelity local inpainting via masked diffusion. Unlike prior approaches that retrain large diffusion backbones or overwrite entire regions, LexiAlign performs \\emph{character-level targeted repair} while preserving surrounding visual context and style. To support systematic training and evaluation, we construct \\textbf{SynOCRText}, a 120k-sample benchmark covering 8 languages, over 20 fonts, diverse layouts, and fine-grained error masks. On SynOCRText, LexiAlign achieves \\textbf{88.4\\% OCR accuracy} (+6.3\\% over the best baseline), Contrastive Language-Image Pretraining (CLIP) Score of 0.852 (+0.023), Peak Signal-to-Noise Ratio (PSNR) of 30.92\\,dB (+2.51\\,dB), and Structural Similarity Index Measure (SSIM) of 0.893 (+0.020). 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