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.
