{"title":"Automatic Prescription Recognition System","authors":"Yang-Yen Ou, Shih-Pang Tseng, Jiun Lin, Xuan-Ping Zhou, Jhing-Fa Wang, Ta-Wen Kuan","doi":"10.1109/ICOT.2018.8705828","DOIUrl":null,"url":null,"abstract":"The study is proposed an automatic prescription recognition system. The system is developed for the visual system of pharmacy robotic technology. Automatic prescription recognition system can help pharmacist to identify medical information in the prescription. We proposed a recognition flow to recognize the information in the prescription. By this system, we can recognize medicines and their usage, routes of administration, dosage, quantity and day. Using the prescription we collected from different hospital, the prescription recognition system reach accuracy of 92.4%.","PeriodicalId":402234,"journal":{"name":"2018 International Conference on Orange Technologies (ICOT)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Conference on Orange Technologies (ICOT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICOT.2018.8705828","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
The study is proposed an automatic prescription recognition system. The system is developed for the visual system of pharmacy robotic technology. Automatic prescription recognition system can help pharmacist to identify medical information in the prescription. We proposed a recognition flow to recognize the information in the prescription. By this system, we can recognize medicines and their usage, routes of administration, dosage, quantity and day. Using the prescription we collected from different hospital, the prescription recognition system reach accuracy of 92.4%.
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