Extraction of Medicine Names from Prescriptions Using Scanning Techniques

In the modern healthcare ecosystem, prescription digitization plays a crucial role in improving medication management, reducing errors, and enhancing accessibility. This paper presents a system that integrates Optical Character Recognition (OCR) and deep learning techniques to automate the extraction and processing of handwritten prescriptions. The system accurately converts prescription images into structured, analyzable, and searchable digital data, facilitating seamless medicine ordering, dosage tracking, and pharmacy interactions. Leveraging state-of-the-art neural networks and natural language processing, the system enhances the accuracy of handwritten text recognition, even in complex medical scripts. This paper presents the underlying architecture, methodology, and efficiency of the prescription scanning module while discussing the challenges associated with handwritten medical text recognition. Additionally, we highlight future research directions to further refine OCR accuracy in the healthcare domain.