Preserving medical information from doctor's prescription ensuring relation among the terminology

Healthcare is a fundamental human right, yet accessing proper healthcare remains a significant challenge. Many patients still rely on physical documents, requiring them to carry all relevant medical records during consultations. Existing methods for extracting data from medical prescriptions have primarily focused on recognizing medication names using manual image annotation or binarization techniques. These approaches often fail to capture detailed prescription information, struggle with multilingual text, and lack the ability to structure medicine-related data comprehensively. To address these limitations, we propose an advanced Electronic Health Record (EHR) system that provides a secure and accessible digital platform for storing and managing patients' medical histories. Our study implements the best Region of Interest (ROI) detection model using the You Only Look Once (YOLO) framework, achieving 99.6% detection accuracy. The extracted ROI is processed using the best Optical Character Recognition (OCR) technique to digitize prescription data, which is then organized into a structured format within a central database. Furthermore, the system integrates a spell correction algorithm with a 96% accuracy rate to rectify misspelled medication names. Beyond text extraction, the system links corrected medication names with dosages, instructions, and manufacturer information, combining generic and brand details to ensure precise and comprehensive healthcare data management. This integrated solution enhances medication data organization, facilitates better healthcare delivery, and improves patient outcomes by streamlining prescription handling and ensuring accurate medication administration. Our EHR system bridges the gap between physical and digital records, advancing an efficient and reliable healthcare ecosystem.