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DC poleHodnotaJazyk
dc.contributor.authorMathihalli, Nidhi
dc.contributor.editorSkala, Václav
dc.date.accessioned2022-09-02T09:58:12Z
dc.date.available2022-09-02T09:58:12Z
dc.date.issued2022
dc.identifier.citationWSCG 2022: full papers proceedings: 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 265-272.en
dc.identifier.isbn978-80-86943-33-6
dc.identifier.issn2464-4617
dc.identifier.urihttp://hdl.handle.net/11025/49604
dc.format8 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.subjectcenově dostupný detektor bankovekcs
dc.subjectefektivní čtečka penězcs
dc.subjectpřesné algoritmy založené na počítačovém viděnícs
dc.titleA Physical Device to Help the Visually Impaired Read Money Using AI/Machine Learning in Third World Countriesen
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThere are over 285 million blind people in the world, with approximately 87% of them living in developing countries. However, in the third world countries, there is currently very little technology to help the visually impaired, especially with financial independence. In this article we present the machine learning algorithms used to develop the device to help visually impaired distinguish between different forms of currency. Using the various currency images, we form a data set that is used to train the transfer learning model. Experimental results show over 94% accuracy with transfer learning model. The device is designed to be portable and hand-held. The device can distinguish between 1, 5, 10, and 20 dollar currency bills. Additionally, the model can work offline. Overall, the device is cost effective, portable, and can be used in the absence of internet connectivity.en
dc.subject.translatedaffordable bill detectoren
dc.subject.translatedeffective money readeren
dc.subject.translatedcomputer vision-based accurate algorithmsen
dc.identifier.doihttps://www.doi.org/10.24132/CSRN.3201.33
dc.type.statusPeer-revieweden
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