Automated Prescription Analysis and Alternative Drug Recommendation System Using OCR and NLP
DOI:
https://doi.org/10.64137/31079911/IJMST-V1I2P101Keywords:
Optical Character Recognition (OCR), Natural Language Processing (NLP), Automated Prescription Management, Handwritten Prescription Recognition, Semantic Analysis, Transformer Models, Medical Text Processing, Healthcare Automation, Image Preprocessing, Handwriting Recognition, Deep Learning, Neural Networks, Drug Recommendation System, Data Security, Healthcare Data Privacy, Medical Electronics, Healthcare Technology, AI in Healthcare, OCR-NLP Integration, Pharmacy Workflow OptimizationAbstract
The integration of Optical Character Recognition (OCR) and Natural Language Processing (NLP) has revolutionized medical applications, particularly in automating prescription management systems. This paper presents a comprehensive system architecture that combines advanced OCR techniques for handwritten prescription recognition with NLP methodologies for semantic extraction and processing of medical text. The proposed solution effectively addresses the challenges posed by handwritten prescription variability, complex medical terminologies, and diverse pharmacy settings. Detailed technical implementations, including image preprocessing, model training, and data annotation strategies, are discussed. Experimental results validate the system's efficacy, highlighting significant improvements in accuracy, operational efficiency, and error reduction compared to existing manual and automated methods. Case studies and user feedback from pharmacy deployments demonstrate practical advantages and challenges, providing insights into system impact and performance. The paper concludes with a discussion on limitations, regulatory considerations, and future directions for integrating advanced AI technologies to further enhance automated prescription management
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