Uncertainty-Aware Bilingual License Plate Recognition with Confidence Propagation for Data-Driven Urban Traffic Forecasting

Uncertainty-Aware Bilingual License Plate Recognition with Confidence Propagation for Data-Driven Urban Traffic Forecasting | 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 Uncertainty-Aware Bilingual License Plate Recognition with Confidence Propagation for Data-Driven Urban Traffic Forecasting Sujit Bhattarai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9212377/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 Urban transportation analytics increasingly rely on automated sensing infrastructures to generate actionable insights for planning and management. However, Automatic Vehicle Number Plate Recognition (ANPR) systems suffer performance degradation when applied to embossed license plates in developing-country environments, where illumination variability, bilingual scripts, and infrastructure idiosyncrasies introduce significant noise. This study proposes an uncertainty-aware, analytics-driven framework that integrates deep learning–based detection, bilingual optical character recognition, and structured urban intelligence modeling for end-to-end traffic analytics. We curate a bespoke dataset of 5,247 embossed Nepali license plate images captured across heterogeneous environmental conditions. Vehicle detection is performed using YOLOv8, and bilingual OCR is implemented via PaddleOCR with script-aware confidence calibration. Recognized events are aggregated to form structured event logs supporting statistical traffic density estimation and short-term forecasting using autoregressive integrated moving average (ARIMA) models with quantified uncertainty. Our empirical evaluation across five independent stratified random train-test splits shows mean detection [email protected] of 96.3 ± 0.85% (daytime: 98.2%), overall recognition accuracy of 91.2 ± 1.62%, with statistically significant improvements over baseline configurations (paired t = 3.87, p = 0.0023 across all splits). Forecasting results achieve MAE = 14.3 ± 1.05 vehicles/hour and RMSE = 21.8 ± 1.42 vehicles/hour (39.4% error reduction vs. naïve persistence baseline), while prediction intervals capture 93% of observed values at 95% confidence. We further demonstrate that propagating recognition confidence into density estimation reduces aggregation bias under adverse conditions. The proposed framework extends conventional ANPR pipelines by embedding recognition uncertainty into downstream analytics, thereby providing a scalable data-driven foundation for intelligent transportation systems in resource-constrained urban environments. Artificial Intelligence and Machine Learning Analysis Robotics Graphical Systems Information Retrieval and Management Automated Number Plate Recognition Traffic Forecasting Uncertainty Quantification ARIMA Modeling Intelligent Transportation Systems Structured Event Analytics Deep Learning Confidence Propagation 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. 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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-9212377","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611398461,"identity":"645563fa-ee86-4307-95a7-b2b830fc0674","order_by":0,"name":"Sujit Bhattarai","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0002-1482-4425","institution":"Soch College of IT, Tribhuvan University","correspondingAuthor":true,"prefix":"","firstName":"Sujit","middleName":"","lastName":"Bhattarai","suffix":""}],"badges":[],"createdAt":"2026-03-24 13:12:12","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-9212377/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9212377/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105565328,"identity":"011d3b75-b3cb-414c-8378-16d57e799b13","added_by":"auto","created_at":"2026-03-27 12:52:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":792331,"visible":true,"origin":"","legend":"","description":"","filename":"UncertaintyAwareLicensePlateRecognitionforDataDrivenUrbanTrafficAnalytics.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9212377/v1_covered_2f76e2f6-4739-4d40-bcee-4e033d178d53.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eUncertainty-Aware Bilingual License Plate Recognition with Confidence Propagation for Data-Driven Urban Traffic Forecasting\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Tribhuvan University","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":"Automated Number Plate Recognition, Traffic Forecasting, Uncertainty Quantification, ARIMA Modeling, Intelligent Transportation Systems, Structured Event Analytics, Deep Learning, Confidence Propagation","lastPublishedDoi":"10.21203/rs.3.rs-9212377/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9212377/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUrban transportation analytics increasingly rely on automated sensing infrastructures to generate actionable insights for planning and management. However, Automatic Vehicle Number Plate Recognition (ANPR) systems suffer performance degradation when applied to embossed license plates in developing-country environments, where illumination variability, bilingual scripts, and infrastructure idiosyncrasies introduce significant noise. This study proposes an uncertainty-aware, analytics-driven framework that integrates deep learning\u0026ndash;based detection, bilingual optical character recognition, and structured urban intelligence modeling for end-to-end traffic analytics.\u003c/p\u003e \u003cp\u003eWe curate a bespoke dataset of 5,247 embossed Nepali license plate images captured across heterogeneous environmental conditions. Vehicle detection is performed using YOLOv8, and bilingual OCR is implemented via PaddleOCR with script-aware confidence calibration. Recognized events are aggregated to form structured event logs supporting statistical traffic density estimation and short-term forecasting using autoregressive integrated moving average (ARIMA) models with quantified uncertainty.\u003c/p\u003e \u003cp\u003eOur empirical evaluation across five independent stratified random train-test splits shows mean detection [email protected] of 96.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85% (daytime: 98.2%), overall recognition accuracy of 91.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62%, with statistically significant improvements over baseline configurations (paired t\u0026thinsp;=\u0026thinsp;3.87, p\u0026thinsp;=\u0026thinsp;0.0023 across all splits). Forecasting results achieve MAE\u0026thinsp;=\u0026thinsp;14.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05 vehicles/hour and RMSE\u0026thinsp;=\u0026thinsp;21.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.42 vehicles/hour (39.4% error reduction vs. na\u0026iuml;ve persistence baseline), while prediction intervals capture 93% of observed values at 95% confidence. We further demonstrate that propagating recognition confidence into density estimation reduces aggregation bias under adverse conditions.\u003c/p\u003e \u003cp\u003eThe proposed framework extends conventional ANPR pipelines by embedding recognition uncertainty into downstream analytics, thereby providing a scalable data-driven foundation for intelligent transportation systems in resource-constrained urban environments.\u003c/p\u003e","manuscriptTitle":"Uncertainty-Aware Bilingual License Plate Recognition with Confidence Propagation for Data-Driven Urban Traffic Forecasting","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-25 08:15:08","doi":"10.21203/rs.3.rs-9212377/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"cd00934e-5065-4a94-91ca-589987e33d1b","owner":[],"postedDate":"March 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65055085,"name":"Artificial Intelligence and Machine Learning"},{"id":65055086,"name":"Analysis"},{"id":65055087,"name":"Robotics"},{"id":65055088,"name":"Graphical Systems"},{"id":65055089,"name":"Information Retrieval and Management"}],"tags":[],"updatedAt":"2026-03-25T08:15:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-25 08:15:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9212377","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9212377","identity":"rs-9212377","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}