The Research on Multi-Granularity Writer and Writing Style Identification for Interpretability

The Research on Multi-Granularity Writer and Writing Style Identification for Interpretability | 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 The Research on Multi-Granularity Writer and Writing Style Identification for Interpretability Shuo Zhang, Zhenjiang Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6538759/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Writer identification is a significant method in biometric identification, with extensive research conducted by numerous scholars. Currently, the predominant approach involves utilizing deep learning technologies for identification. While these methods achieve high accuracy, their black-box nature presents challenges in fields such as judiciary and finance, where security demands are stringent, leading to issues with the lack of interpretability of identification results. To address this, the following contributions are made in this study: 1. A multi-granularity data augmentation strategy is introduced, ensuring the model’s capability to perceive both global and local features. 2. A multi-task writer identification framework is designed, which can simultaneously identify the writer’s identity and style. 3. An interpretative strategy centered around the consistency of the writer’s identity and style is developed to provide interpretability for the identification results. Experiments conducted on multiple public datasets demonstrate that our approach can accurately identify the writer’s identity and style, with a degree of interpretability in the results. Writer Identification Writing Style Identification Graph Neural Networks Multi-Granularity Data Augmentation Interpretability Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 08 May, 2026 Reviewers agreed at journal 15 Jul, 2025 Reviewers agreed at journal 15 Jul, 2025 Reviewers invited by journal 14 Jul, 2025 Editor assigned by journal 03 May, 2025 Submission checks completed at journal 28 Apr, 2025 First submitted to journal 27 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. 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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-6538759","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485730238,"identity":"2451a98d-f865-40ab-bb24-4e4806cff26f","order_by":0,"name":"Shuo Zhang","email":"","orcid":"","institution":"Gansu University of Political Science and Law","correspondingAuthor":false,"prefix":"","firstName":"Shuo","middleName":"","lastName":"Zhang","suffix":""},{"id":485730239,"identity":"827b515a-c1b6-4248-8c09-21e4b37e3317","order_by":1,"name":"Zhenjiang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIie3RvcrCMBSA4VMCcTnawaXgT28hpVAX8Ra8hRShXer8fYNDwVV0rZO3UBfnQEGXXoBjps69An+SWWJHh7xLOJCHwAmAzfaDDQBi2eJmqicJ4OTfCAVYBcX4GuqJdyR0iHMS592JKxqnyGh6WtaR5DCflII00vyKSGRbj9fnezZjHJKwFHTGjMTJ0+C4o+tzgZHHoYpLgdQzEgLJqP8gaXCoFXl2IPRNEAn3IVNEdCColozXoPSSP8bZKjxWNDIS1631V/r+obrI9n8x2d+2jZEAINcHE+qP1KqI+f67ntCHnytis9lstg+9AN8LRgMZonTcAAAAAElFTkSuQmCC","orcid":"","institution":"Gansu University of Political Science and Law","correspondingAuthor":true,"prefix":"","firstName":"Zhenjiang","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-04-27 07:38:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6538759/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6538759/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86931443,"identity":"68e43fef-eb11-45b9-b2c8-048df6906084","added_by":"auto","created_at":"2025-07-17 09:45:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6657133,"visible":true,"origin":"","legend":"","description":"","filename":"TheResearchonMultiGranularityWriterandWritingStyleIdentificationforInterpretability.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6538759/v1_covered_601c2fb8-a402-4cef-9d56-87925bc0e1b7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Research on Multi-Granularity Writer and Writing Style Identification for Interpretability","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"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":"Writer Identification, Writing Style Identification, Graph Neural Networks, Multi-Granularity Data Augmentation, Interpretability","lastPublishedDoi":"10.21203/rs.3.rs-6538759/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6538759/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWriter identification is a significant method in biometric identification, with extensive research conducted by numerous scholars. 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