Word-level Afan Oromo Sign Language Recognition Using Deep Learning Approach

Word-level Afan Oromo Sign Language Recognition Using Deep Learning Approach | 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 Article Word-level Afan Oromo Sign Language Recognition Using Deep Learning Approach Solomon Endalu, Kula Kakeba This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9060917/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 S ign language is a primary communication mode for the hearing-impaired community, yet barriers persist due to limited sign language proficiency among the hearing population and a scarcity of effective translation tools. This work addresses the critical need for improved communication accessibility by developing a real-time Afan Oromo sign language recognition. A primary challenge lies in the absence of comprehensive research on Afan Oromo sign language recognition and translation. To bridge this gap, this study proposes a novel approach utilizing the YOLOv10 model, enhanced for sign language recognition and translation. By leveraging a diverse dataset of 70 common sign language words, we perform data pre-processing steps such as frame extraction, resizing, cropping, flipping, normalization and data splitting to optimize model performance. The core contribution of this research is the development of a robust sign language recognition model capable of accurately translating Afan Oromo signs into text. We achieved impressive results with a Total Average Precision of 94.12%, Recall of 95.01%, and mAP@50 of 90.03% on the YOLOv10 model. This would enable accessible translation tools to be developed for the Afaan Oromo sign language community which could contribute towards improving communication and in- clusivity in their use of signing as a mode of expression. Physical sciences/Engineering Physical sciences/Mathematics and computing Afan Oromo Signed Language Deep Learning Computer Vision and CNN YOLOv10 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. 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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-9060917","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":611395726,"identity":"2c67e367-ac53-4bec-921e-7b3f04534259","order_by":0,"name":"Solomon Endalu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYFAC5gYQKWd/vCHxAZDBw0dYCyNYizHDmQOPDUBa2IjVkthwI/GZBIhFUIt8e2PjoxsVdxgbew6nVX7NsZNhY2B++OgGHi0GZw42G+ececbMzN6Wdlt2WzLQYWzGxjn4tEgktknnth1mY+M5k3ZbchszUAsPmzQ+LfIzIFp4eCTyvxVLbqsnrIXhBkSLhIREQhrjx22HCWuB+uWwgQHPgWRpxm3HediYCfhFvr354OOcisP1G9gbEj/+3FZtz8/e/PAxXochA2YeMEmschBg/EGK6lEwCkbBKBgxAABRZ0rT3HGPOQAAAABJRU5ErkJggg==","orcid":"","institution":"Addis Ababa Science and Technology University","correspondingAuthor":true,"prefix":"","firstName":"Solomon","middleName":"","lastName":"Endalu","suffix":""},{"id":611395730,"identity":"fb8122b0-b794-4aca-9a5d-0386078fa3bc","order_by":1,"name":"Kula Kakeba","email":"","orcid":"","institution":"Addis Ababa Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Kula","middleName":"","lastName":"Kakeba","suffix":""}],"badges":[],"createdAt":"2026-03-07 21:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9060917/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9060917/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107140905,"identity":"8acf4b81-23e4-4af4-9008-1a2cd94f2465","added_by":"auto","created_at":"2026-04-17 08:58:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1112507,"visible":true,"origin":"","legend":"","description":"","filename":"WLAOSLRManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9060917/v1_covered_29887bc8-a55b-445b-b402-56c840250456.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eWord-level Afan Oromo Sign Language Recognition Using Deep Learning Approach\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"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":"Afan Oromo Signed Language, Deep Learning, Computer Vision, and CNN, YOLOv10","lastPublishedDoi":"10.21203/rs.3.rs-9060917/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9060917/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eS\u003c/b\u003eign language is a primary communication mode for the hearing-impaired community, yet barriers persist due to limited sign language proficiency among the hearing population and a scarcity of effective translation tools. 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