TCG-AI: AI Integration in Collectibles Grading for Trading Card Games and Sport Cards

TCG-AI: AI Integration in Collectibles Grading for Trading Card Games and Sport Cards | 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 TCG-AI: AI Integration in Collectibles Grading for Trading Card Games and Sport Cards Luke Warren, Kevin Pimbblett, Julius Mboli This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8098898/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 grading of trading cards and collectibles remains a labour-intensive and subjective task, traditionally dependent on expert visual inspection. This paper investigates the integration of Artificial Intelligence (AI) into automated grading systems, with a focus on balancing accuracy, scalability and interpretability. We compare conventional image-processing methods based on OpenCV with modern Convolutional Neural Network (CNN) models and propose a hybrid framework that incorporates YOLOv8 with Oriented Bounding Box (OBB) support for localised defect detection. The system operates through a six-stage pipeline encompassing centring, corner wear, edge integrity, surface quality and holistic scoring. To support reproducibility and consistency, we developed a robotic imaging platform capable of capturing standardised high-resolution images (6000 × 4000 pixels) under controlled lighting conditions, enhanced by Flat Field Correction (FFC) for illumination normalisation. Using this platform, we constructed a large-scale dataset comprising over 43,000 training samples after augmentation, covering a wide range of real-world card conditions. Experimental evaluation using localisation accuracy, region-level grading agreement and professional grading consistency metrics demonstrates that CNN-based classification approaches significantly outperform traditional OpenCV pipelines in predictive accuracy but lack spatial interpretability. In contrast, the proposed hybrid framework achieves improved defect localisation and grading consistency relative to CNN-only baselines, while providing transparent visual explanations through YOLO-OBB detections. The integration of deterministic image analysis with deep learning enables both quantitative subgrades and interpretable defect localisation, offering an explainable and scalable solution for automated collectible card grading. Artificial Intelligence Convolutional Neural Network YOLO Trading Cards Grading Explainable AI 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-8098898","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591041363,"identity":"a99ffd28-f942-40b6-9e1d-7ef7ba508bde","order_by":0,"name":"Luke Warren","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYBACPjiLvbHxwYeKAwx87BC+AS4tbHAWz+HDhjPOHGBgYyZai0RamjRvG1FaeAw/V+6xyWNgyDGQ4J13J7GNmYHxww+Gw8Z4tBhLnnmWVszAcMbAQHLbM5AWZskehsNmuLXwbpBsOHA4cf/BHoMEw22HQVoYpBkYDtvg0bL5Z8OB/4kNzDwGBxLngLUw/yagZRvQlgOJDWxsiQ0HG8Ba2EC24HYYM/83y4YDyYkNPMyHGRuOHTZuY2Zss+wxSMfpfX72tuSbDQfsEhvkH7b//lNzWLafvfnwjR8V1oYNuPQwYwoxNuCJlVEwCkbBKBgFxAAA3upRn6aDTZcAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Luke","middleName":"","lastName":"Warren","suffix":""},{"id":591041364,"identity":"e7e489fd-6fce-460f-8c52-3adbefd755d7","order_by":1,"name":"Kevin Pimbblett","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Kevin","middleName":"","lastName":"Pimbblett","suffix":""},{"id":591041365,"identity":"8698811f-62b3-4e2d-8174-d610c3776179","order_by":2,"name":"Julius Mboli","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Julius","middleName":"","lastName":"Mboli","suffix":""}],"badges":[],"createdAt":"2025-11-12 17:53:21","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-8098898/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8098898/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[],"financialInterests":"No competing interests reported.","formattedTitle":"TCG-AI: AI Integration in Collectibles Grading for Trading Card Games and Sport Cards","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Artificial Intelligence, Convolutional Neural Network, YOLO, Trading Cards, Grading, Explainable AI","lastPublishedDoi":"10.21203/rs.3.rs-8098898/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8098898/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe grading of trading cards and collectibles remains a labour-intensive and subjective task, traditionally dependent on expert visual inspection. This paper investigates the integration of Artificial Intelligence (AI) into automated grading systems, with a focus on balancing accuracy, scalability and interpretability. We compare conventional image-processing methods based on OpenCV with modern Convolutional Neural Network (CNN) models and propose a hybrid framework that incorporates YOLOv8 with Oriented Bounding Box (OBB) support for localised defect detection.\u003c/p\u003e\n\u003cp\u003eThe system operates through a six-stage pipeline encompassing centring, corner wear, edge integrity, surface quality and holistic scoring. To support reproducibility and consistency, we developed a robotic imaging platform capable of capturing standardised high-resolution images (6000 × 4000 pixels) under controlled lighting conditions, enhanced by Flat Field Correction (FFC) for illumination normalisation. 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