C. Paglinawan, Marielle Hannah Caliolio, Joshua Frias
{"title":"使用YOLOv4和Tesseract OCR进行药物分类","authors":"C. Paglinawan, Marielle Hannah Caliolio, Joshua Frias","doi":"10.1109/ICCAE56788.2023.10111387","DOIUrl":null,"url":null,"abstract":"For seniors, consuming the incorrect pharmaceutical drug is a common but serious medical issue. Poor vision, difficulty reading, impaired memory, and problems with fine-motor skills are all factors that might lead to using the incorrect pharmaceutical drug. Previous studies have created systems that can automatically identify medications to address this issue. Although, this research lacked the integration of the latest official version of YOLO and the Tesseract OCR. As such, the researchers developed a system that can classify medicines using YOLOv4 and Tesseract OCR. Through tests, YOLOv4 was established to be the best in accuracy (~95% accuracy), followed by YOLOv4 and Tesseract OCR together (~19% accuracy). The least accurate one was using the Tesseract OCR only (~14% accuracy).","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Medicine Classification Using YOLOv4 and Tesseract OCR\",\"authors\":\"C. Paglinawan, Marielle Hannah Caliolio, Joshua Frias\",\"doi\":\"10.1109/ICCAE56788.2023.10111387\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For seniors, consuming the incorrect pharmaceutical drug is a common but serious medical issue. Poor vision, difficulty reading, impaired memory, and problems with fine-motor skills are all factors that might lead to using the incorrect pharmaceutical drug. Previous studies have created systems that can automatically identify medications to address this issue. Although, this research lacked the integration of the latest official version of YOLO and the Tesseract OCR. As such, the researchers developed a system that can classify medicines using YOLOv4 and Tesseract OCR. Through tests, YOLOv4 was established to be the best in accuracy (~95% accuracy), followed by YOLOv4 and Tesseract OCR together (~19% accuracy). The least accurate one was using the Tesseract OCR only (~14% accuracy).\",\"PeriodicalId\":406112,\"journal\":{\"name\":\"2023 15th International Conference on Computer and Automation Engineering (ICCAE)\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-03-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 15th International Conference on Computer and Automation Engineering (ICCAE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCAE56788.2023.10111387\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCAE56788.2023.10111387","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Medicine Classification Using YOLOv4 and Tesseract OCR
For seniors, consuming the incorrect pharmaceutical drug is a common but serious medical issue. Poor vision, difficulty reading, impaired memory, and problems with fine-motor skills are all factors that might lead to using the incorrect pharmaceutical drug. Previous studies have created systems that can automatically identify medications to address this issue. Although, this research lacked the integration of the latest official version of YOLO and the Tesseract OCR. As such, the researchers developed a system that can classify medicines using YOLOv4 and Tesseract OCR. Through tests, YOLOv4 was established to be the best in accuracy (~95% accuracy), followed by YOLOv4 and Tesseract OCR together (~19% accuracy). The least accurate one was using the Tesseract OCR only (~14% accuracy).