An End-to-End Pipeline for Handwritten Prescription Understanding: OCR–NLP Integration and Error Propagation Analysis

Abstract

Interpreting handwritten medical prescriptions is a constant challenge in healthcare workflows. This often leads to patient confusion, medication errors, and delays in receiving proper treatment. Even with improvements in digital health systems, reliably understanding prescriptions is limited by differences in handwriting quality and the complexity of medical language. This paper introduces a complete pipeline for understanding handwritten prescriptions. It combines Optical Character Recognition (OCR) with clinical Natural Language Processing (NLP) to turn unstructured prescription images into organized medical information. The proposed framework focuses on system robustness by examining how errors from OCR affect downstream NLP-based medication extraction. To ensure a realistic and repeatable evaluation while respecting privacy, the system is tested using publicly available handwritten text and clinical NLP benchmark datasets. The experimental analysis looks at error variability, performance in entity-level extraction, and how the integration of OCR and NLP affects prescription interpretation. The results show that while OCR and clinical NLP perform reasonably on their own, their interaction is vital for overall system reliability. These findings underline the need for joint evaluation and error-conscious design in automated prescription processing systems. This supports the creation of better, patient-focused digital healthcare solutions.