International Journal on Science and Technology

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AI-Powered Prescription Decoder and Generic Medicine Recommender

Author(s) Ms. Sravanthi Thota, Ms. Shirisha K
Country India
Abstract Handwritten medical prescriptions are often difficult to interpret due to illegible handwriting, inconsistent formats, and the use of medical abbreviations. Misinterpretation of prescriptions can lead to medication errors, incorrect dosage, and serious health risks. Additionally, patients are often unaware of affordable generic alternatives to prescribed branded medicines, resulting in increased healthcare expenses. This project presents an AI-Powered Prescription Decoder and Generic Medicine Recommender, an intelligent system designed to automatically extract and interpret handwritten prescription information and provide cost-effective and safe medication alternatives. The system integrates deep learning-based OCR models such as CRAFT (Character Region Awareness for Text Detection) and CRNN (Convolutional Recurrent Neural Network) to detect and recognize handwritten text from prescription images. The extracted text is processed using NLP techniques to identify key medical entities such as medicine names, dosage, frequency, and duration. The system then maps recognized medicines to standardized drug databases such as RxNorm to recommend equivalent generic medicines. Additional features include price comparison between branded and generic drugs, side effect and drug interaction warnings using SIDER, and confidence score display to indicate OCR reliability.
Keywords AI, Prescription Decoder, Handwritten Text Recognition, CRAFT, CRNN, LLM, Generic Medicine Recommendation, OCR, NLP, Drug Validation, Medical Data Processing.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 17, Issue 2, April-June 2026
Published On 2026-04-28
DOI https://doi.org/10.71097/IJSAT.v17.i2.10892
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