Associative Modeling of Chinese Character Stroke Sequences Combining Transformer and Geometric Constraints

Associative Modeling of Chinese Character Stroke Sequences Combining Transformer and Geometric Constraints | 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 Associative Modeling of Chinese Character Stroke Sequences Combining Transformer and Geometric Constraints Yan Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8153689/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Mar, 2026 Read the published version in International Journal of Data Science and Analytics → Version 1 posted 14 You are reading this latest preprint version Abstract This research introduces a combined deep learning framework for classifying Chinese stroke sequences using the tools of multi-scale spatial encoding, hierarchical attention modeling, and geometric constraint learning. Each stroke sample is encoded to a 2D trajectory-density feature map to retain spatial continuity, temporal progression, and fine-grained geometric structure. The U-Net framework enables the extraction of multi-resolution spatial features, while the Swin Transformer captures long-range contextual dependencies with shifted-window self-attention. A geometric constraint loss term is included to capture curvature smoothness, directionality consistency, and structural fidelity to address the challenges of visual similarity and the significant variability of handwritten strokes. Experiments conducted with four main stroke classes-heng, shu, pie, and na demonstrated strong classification performance at 98.6% accuracy, average precision of over 0.994, and AUC of over 0.995. Evaluations with the confusion matrix, ROC curves, precision–recall curves, and error rate (FPR/FNR) metrics each establish the robustness and generalizability of the model across different handwriting styles. These findings indicate that the framework effectively captures stroke-level spatial–temporal patterns and provides a robust basis for downstream applications, including character reconstruction, handwriting analysis, digital calligraphy, and intelligent writing-education systems. Chinese Character Stroke Sequences Classification U-Net Swin Transformer Geometric Constraints Handwriting Analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 01 Mar, 2026 Read the published version in International Journal of Data Science and Analytics → Version 1 posted Editorial decision: Revision requested 30 Dec, 2025 Reviews received at journal 28 Dec, 2025 Reviews received at journal 28 Dec, 2025 Reviews received at journal 23 Dec, 2025 Reviews received at journal 22 Dec, 2025 Reviewers agreed at journal 21 Dec, 2025 Reviewers agreed at journal 20 Dec, 2025 Reviewers agreed at journal 18 Dec, 2025 Reviewers agreed at journal 18 Dec, 2025 Reviewers agreed at journal 18 Dec, 2025 Reviewers invited by journal 18 Dec, 2025 Editor assigned by journal 20 Nov, 2025 Submission checks completed at journal 20 Nov, 2025 First submitted to journal 19 Nov, 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-8153689","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":563670220,"identity":"47d3d592-9b4a-44ce-aa0f-0e592429dd9a","order_by":0,"name":"Yan 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Constraints","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":"international-journal-of-data-science-and-analytics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jdsa","sideBox":"Learn more about [International Journal of Data Science and Analytics](http://link.springer.com/journal/41060)","snPcode":"41060","submissionUrl":"https://submission.nature.com/new-submission/41060/3","title":"International Journal of Data Science and 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Each stroke sample is encoded to a 2D trajectory-density feature map to retain spatial continuity, temporal progression, and fine-grained geometric structure. The U-Net framework enables the extraction of multi-resolution spatial features, while the Swin Transformer captures long-range contextual dependencies with shifted-window self-attention. A geometric constraint loss term is included to capture curvature smoothness, directionality consistency, and structural fidelity to address the challenges of visual similarity and the significant variability of handwritten strokes. Experiments conducted with four main stroke classes-heng, shu, pie, and na demonstrated strong classification performance at 98.6% accuracy, average precision of over 0.994, and AUC of over 0.995. Evaluations with the confusion matrix, ROC curves, precision\u0026ndash;recall curves, and error rate (FPR/FNR) metrics each establish the robustness and generalizability of the model across different handwriting styles. These findings indicate that the framework effectively captures stroke-level spatial\u0026ndash;temporal patterns and provides a robust basis for downstream applications, including character reconstruction, handwriting analysis, digital calligraphy, and intelligent writing-education systems.\u003c/p\u003e","manuscriptTitle":"Associative Modeling of Chinese Character Stroke Sequences Combining Transformer and Geometric Constraints","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-23 16:53:39","doi":"10.21203/rs.3.rs-8153689/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-30T10:29:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-29T02:45:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-28T20:21:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-23T05:46:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-23T04:54:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"89526720173480726130583589138919465234","date":"2025-12-21T15:13:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"312107632711761052328754488570298785907","date":"2025-12-20T05:28:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"39161638675023914825292559314372982762","date":"2025-12-19T03:32:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"301009534782897295004357635480247372769","date":"2025-12-18T12:15:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99035041803806298390363446638742263334","date":"2025-12-18T06:23:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-18T05:38:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-20T16:04:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-20T14:24:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Data Science and Analytics","date":"2025-11-19T09:44:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-data-science-and-analytics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jdsa","sideBox":"Learn more about [International Journal of Data Science and Analytics](http://link.springer.com/journal/41060)","snPcode":"41060","submissionUrl":"https://submission.nature.com/new-submission/41060/3","title":"International Journal of Data Science and Analytics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e04516d3-2ce2-445d-8fbd-ca2cd8ab6a43","owner":[],"postedDate":"December 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T16:05:53+00:00","versionOfRecord":{"articleIdentity":"rs-8153689","link":"https://doi.org/10.1007/s41060-026-01052-6","journal":{"identity":"international-journal-of-data-science-and-analytics","isVorOnly":false,"title":"International Journal of Data Science and Analytics"},"publishedOn":"2026-03-01 15:58:19","publishedOnDateReadable":"March 1st, 2026"},"versionCreatedAt":"2025-12-23 16:53:39","video":"","vorDoi":"10.1007/s41060-026-01052-6","vorDoiUrl":"https://doi.org/10.1007/s41060-026-01052-6","workflowStages":[]},"version":"v1","identity":"rs-8153689","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8153689","identity":"rs-8153689","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}