Medical prescriptions that are challenging to interpret present significant issues for the healthcare industry because they increase the possibility of errors in patient care and medication administration. This study presents an efficient workflow that uses Optical Character Recognition (OCR) technology, specifically, Tesseract OCR, along with a preprocessing step to extract text from handwritten prescriptions. The preprocessing stage uses grayscale conversion, noise reduction, and contrast enhancement to increase the accuracy of OCR. Significant results from experiments on a publicly accessible dataset show that preprocessing greatly improves performance, lowering the error rate from 34.7 to 18.3% and raising average accuracy from 65.3 to 81.7%. The enhanced accuracy outweighs the modest increase in processing time (from 0.8 to 1.2 s), emphasizing the potential of using these techniques in practical healthcare applications. The study’s findings also demonstrated the successful analysis of the text using Natural Language Processing (NLP) and Clinical Bidirectional Encoder Representations from Transformers (ClinicalBERT) techniques by identifying four distinct diseases, Common Cold, Diabetes Mellitus, Bronchitis, and disease caused by Anemia, as validated by a medical professional. This demonstrates the system’s potential to improve health care processes by automatically digitizing handwritten prescriptions.