AI-Driven Automated Prescription and Medicine Verification Framework using Computer Vision

Errors in prescription reading and medication administration remain a critical challenge in modern healthcare systems, often leading to adverse drug events and compromised patient safety. This paper presents an end-to-end framework that utilizes computer vision and optical character recognition (OCR) techniques to automatically verify prescription details against physical pharmaceutical packaging. The system extracts textual details from handwritten and electronic prescriptions, cross-references active drug ingredients, and identifies potential dosage discrepancies or contraindications in real time. Preliminary evaluations demonstrate a 94.6% accuracy rate in text recognition and verification across standard pharmaceutical samples, offering a lightweight, scalable solution to reduce manual medication errors in clinical and retail pharmacy settings.