Enhancing Handwritten Prescription Recognition with AI-Driven OCR

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ID: 312618
2025
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Abstract
Accurate interpretation and understanding of medical prescriptions are crucial for healthcare providers to ensure suitable treatment for patients. However, the increasing number of prescriptions and the complexity of pharmaceutical regimens may lead to errors, which could have severe consequences. To overcome this problem, artificial intelligence (AI) can automate tasks such as identifying the correct medication, determining the correct dose, and checking for drug interactions. This makes prescription analysis more accurate and faster. This study presents an AI-driven optical character recognition (OCR) framework that uses TrOCR with Roboflow to convert handwritten prescriptions into a digital format. Our method achieves a Word Error Rate (WER) of 12.5%, a Character Error Rate (CER) of 8.7%, and an Exact Match Accuracy of 81.3%. These results show that the system can accurately transcribe prescriptions and help reduce medication errors, making healthcare workflows safer and more efficient.
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imported_1777055350_69ebb6763eb96 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Atif Jan
Journal Journal of Computing & Biomedical Informatics
Year 2025
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/1054
Keywords Keywords not found

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