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Title: COINIC-PH: A Philippine new generation series of coin intelligent classification inference approach for visually impaired
Authors: Alvin Alon, Catherine Alimboyong, Philip Ermita, Jaime Pulumbarit, Marlon Hernandez
Journal: ARPN Journal of Engineering and Applied Sciences
Publisher: Khyber Medical College, Peshawar
Country: Pakistan
Year: 2021
Volume: 16
Issue: 19
Language: English
Object detection experiences widespread use in many technology-related fields nowadays. This paper uses computer vision to execute object detection of the new series of coins of the Philippine peso. Compared to the coin designs of the previous series, these coins are much more identical to each other, which can be hard to distinguish for people with bad eyesight. Through the use of object detection, these coins can then be classified into their respective amounts just by an image or video sample. The machine learning model used in this paper performed sufficiently, with it being able to distinguish the one, five, ten, and twenty-peso coins from each other from image and video samples.
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