AUTHOR=Rupa Ch. , Srivastava Gautam , Ganji Bharath , Tatiparthi Sai Praveen , Maddala Karthik , Koppu Srinivas , Chun-Wei Lin Jerry 

TITLE=Medicine Drug Name Detection Based Object Recognition Using Augmented Reality

JOURNAL=Frontiers in Public Health

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.881701

DOI=10.3389/fpubh.2022.881701

ISSN=2296-2565

ABSTRACT=Augmented Reality (AR) is an innovation that empowers most of us to coordinate computerized data into the client's real-world space. It offers an advanced and progressive methodology for medicines and training in medication. AR aids in surgery planning and patient therapy and discloses complex medical circumstances to patients and their family members. With the quick upgrades in innovation, an ever-increasing number of medical records are accessible, which contain a lot of medical data, similar to medical substances and relations between them. To exploit the clinical texts, it is important to separate significant data from them. Drugs, along with some kind of the fundamental clinical components, additionally should be perceived. Drug name acknowledgment (DNR) tries to recognize drugs specified in unstructured clinical texts and order them into predefined classifications, which is utilized to deliver a connected 3D model inside this present reality client space. This work shows the utilization of AR to give an active and visual representation of data about medicines and their applications. The proposed method is a mobile application that uses a native camera and Optical Recognition Algorithm (OCR) to extract the text on the medicines. The extracted text is over and above processed using NLP tools which are then further used to identify the generic name and category of the drug using the dedicated DNR database. 3D model prepared particularly for the drug is then presented in AR using ArCore. The results obtained are encouraging. The proposed method can detect the text with an average time of 0.005 seconds and can produce the visual representation of the output with an average time of 1.5 seconds.