Rx Vision

Abstract

Handwritten prescription detection involves evaluating prescriptions at pharmacies to minimize medication errors. To ensure efficient and accurate screening, technology plays a crucial role. This research aims to develop an artificial intelligence-based system that digitizes and automates handwritten prescriptions, specifically for Pakistan. RxVision addresses the challenges associated with handwritten prescriptions in healthcare by leveraging image recognition, artificial intelligence, and modern camera technology. Misinterpreting handwritten prescriptions poses significant risks to patient safety. Therefore, using advanced algorithms, RxVision aims to convert them into precise digital data. The proposed methodology includes data collection, processing, image recognition, text extraction, algorithm development, and real-time validation. Convolutional Neural Networks (CNNs) are employed for image recognition, while Optical Character Recognition (OCR) is used for text extraction. The system utilizes a comprehensive drug dictionary to enhance classification and ensure prescription accuracy through real-time validation against medication databases. RxVision application areas encompass enhancing patient safety, improving pharmacy efficiency, facilitating doctor-patient communication, and enhancing medical collaboration. The research acknowledges the significance of Optical Character Recognition (OCR) as a prominent research area, particularly in recognizing handwritten characters. Leveraging domain knowledge, the objective is to develop a method that accurately extracts the content of a medical prescription. RxVision represents an innovative solution that has the potential to revolutionize prescription management. It offers a comprehensive approach to enhance patient safety, streamline pharmaceutical operations, and facilitate effective communication in the healthcare industry.