Developing an Assistive Technology for Visually Impaired Persons: Ethiopian Currency Identification

Developing an Assistive Technology for Visually Impaired Persons: Ethiopian Currency Identification | 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 Developing an Assistive Technology for Visually Impaired Persons: Ethiopian Currency Identification Samuel Alamirew, Getnet Kebede This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3859582/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 In this study an innovative assistive technology designed to empower visually-impaired individuals by providing them with the means to independently identify Ethiopian banknotes. Visually-impaired individuals often face challenges in recognizing and differentiating between currency denominations, hindering their financial independence and day-to-day transactions. To address this issue, we developed a novel device that attaches to standard eyeglasses and leverages state-of-the-art deep learning techniques for real-time banknote detection and recognition. An extensive dataset comprising over 15,000 annotated images of five Ethiopian birr denominations (5, 10, 50, 100, and 200 Birr) was used for training. YOLOv5 was selected as the best-performing model, attaining a mAP of 97.9%. Additionally, the study compared YOLOv5 with other popular object detection models such as SSD_MobileNet_v2 and Faster_RCNN_Inception_v2. YOLOv5 outperformed the other models in terms of both accuracy and speed. The research goes beyond the development of the device and extends to the user interface, introducing a mobile application named "Genzebe." This application seamlessly integrates with their device, allowing users to capture images of banknotes, which are then processed for denomination recognition. The application has audio output of denomination to the user. By making use of cost-effective hardware components, including a Raspberry Pi and a Pi Camera, the device is implemented and demonstrated as a practical solution that could significantly enhance the autonomy and financial inclusivity of visually-impaired individuals. In conclusion, this work showcases a highly effective and user-friendly solution for identification of Ethiopian currency. Assistive Technology Computer Vision Convolutional Neural Network (CNN) Visually-Impaired (VI) You Only Look Once (YOLO) Wearable Device (“Smart Glass”) 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-3859582","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":267109002,"identity":"d1fa22d0-96d9-40ae-9284-4430511e724a","order_by":0,"name":"Samuel Alamirew","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIiWNgGAWjYNCCAgYeIMkGxP/lQPwDDwhqMYBrYTYGa0kgQgsDTEtiA4iJT4t8++k0iQ8GtTLy7YefPfjYxpY+P+zwQ6AtdnK6DTjMP5O7TXKGwXEexp40c8OZbTy5G2+nGQC1JBubHcDlpNxt0jwGx3iYGXLYpHm3SeRunJ0A0nIgcRsOLfL9b7dJ/wFqYeN/wyb9d5tBuuHs9A94tTDcANrCYFDDwyMBtIVxW0KCvHQOflsMbrzdbNljcIBHQuKZmWTvvwOGG6RzCg4kGOD2i3x/7sYbPyrq7OX7k59J/DhzQF5+dvrmDx8q7ORwaYGCw0j2HoAECyFQh2RvA0HVo2AUjIJRMMIAACwYX0tXaWA0AAAAAElFTkSuQmCC","orcid":"","institution":"Bahir Dar University","correspondingAuthor":true,"prefix":"","firstName":"Samuel","middleName":"","lastName":"Alamirew","suffix":""},{"id":267109003,"identity":"848a391d-890c-443a-8198-0e475c59f766","order_by":1,"name":"Getnet Kebede","email":"","orcid":"","institution":"Bahir Dar University","correspondingAuthor":false,"prefix":"","firstName":"Getnet","middleName":"","lastName":"Kebede","suffix":""}],"badges":[],"createdAt":"2024-01-13 08:59:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3859582/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3859582/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50463751,"identity":"cab96063-f2bb-412a-b477-8135e14c3bec","added_by":"auto","created_at":"2024-01-31 22:37:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1812658,"visible":true,"origin":"","legend":"","description":"","filename":"samuelmanuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3859582/v1_covered_d959bcbc-623d-4349-83a3-f2eae6c727d7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Developing an Assistive Technology for Visually Impaired Persons: Ethiopian Currency Identification","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":"Assistive Technology, Computer Vision, Convolutional Neural Network (CNN), Visually-Impaired (VI), You Only Look Once (YOLO), Wearable Device (“Smart Glass”)","lastPublishedDoi":"10.21203/rs.3.rs-3859582/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3859582/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this study an innovative assistive technology designed to empower visually-impaired individuals by providing them with the means to independently identify Ethiopian banknotes. Visually-impaired individuals often face challenges in recognizing and differentiating between currency denominations, hindering their financial independence and day-to-day transactions. To address this issue, we developed a novel device that attaches to standard eyeglasses and leverages state-of-the-art deep learning techniques for real-time banknote detection and recognition. An extensive dataset comprising over 15,000 annotated images of five Ethiopian birr denominations (5, 10, 50, 100, and 200 Birr) was used for training. YOLOv5 was selected as the best-performing model, attaining a mAP of 97.9%. Additionally, the study compared YOLOv5 with other popular object detection models such as SSD_MobileNet_v2 and Faster_RCNN_Inception_v2. YOLOv5 outperformed the other models in terms of both accuracy and speed. The research goes beyond the development of the device and extends to the user interface, introducing a mobile application named \"Genzebe.\" This application seamlessly integrates with their device, allowing users to capture images of banknotes, which are then processed for denomination recognition. The application has audio output of denomination to the user. By making use of cost-effective hardware components, including a Raspberry Pi and a Pi Camera, the device is implemented and demonstrated as a practical solution that could significantly enhance the autonomy and financial inclusivity of visually-impaired individuals. 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