Automated medicine billing with image recognition

Abstract

This research aims to revolutionize the manual billing processes in pharmacies through an innovative system leveraging image recognition technology. By using a strategically placed camera, the system captures images of prescribed medications and employs advanced Convolutional Neural Networks (CNNs) to accurately identify key details such as drug names, dosages, and quantities. This extracted information is seamlessly integrated into the pharmacy's existing billing software, thereby automating the billing process and calculating costs with precision. The automation eliminates the need for manual data entry, significantly reducing errors and enhancing operational efficiency. Moreover, the system securely stores both the medication images and the corresponding billing records in a database, ensuring data integrity and providing easy access for future reference. This approach not only speeds up the billing process but also ensures secure and accurate bill printing, contributing to a faster, more precise, and userfriendly pharmacy experience for patients and staff alike. The project ultimately aims to transform pharmacy operations, enabling staff to focus more on customer care rather than administrative tasks and enhancing the overall quality of healthcare delivery. By addressing the limitations of traditional manual billing methods, this system represents a significant step forward in creating a streamlined and efficient healthcare environment.