Real-Time Medication Security: Optical Character Recognition (Ocr) For Automated Drug Cross-Referencing

Abstract Adverse drug reactions (ADRs) driven by pharmacogenomic variability are a significant cause of iatrogenic morbidity. While clinical decision support systems can map genetic variants to drug safety profiles, point-of-care implementation is often hindered by the manual data-entry burden placed on clinicians. This paper presents the design and integration of a computer vision module—the 'Pill Scanner'—within the GenomeGuard web application. The module utilizes Optical Character Recognition (OCR) to automate the identification of medications from physical pill labels via device cameras. By bridging real-time image processing with a pre-computed pharmacogenomic safety matrix, the system algorithmically cross-references extracted text against a patient’s personalized genetic risk profile. We detail the hybrid OCR architecture leveraging both server-side Tesseract processing and client-side fallbacks, the image preprocessing pipeline optimized for poor-quality clinical environments, and the fuzzy-matching logic required for robust drug identification. The implementation demonstrates that integrating computer vision directly into a pharmacogenomic workflow significantly reduces medication verification latency, offering a seamless approach to point-of-care medication security.