Handwritten Medical Note Recognition Using NLP

Abstract

In the healthcare domain, illegible handwriting in
medical prescriptions poses a significant challenge,
often leading to errors in medication
administration and delayed treatment. This project
proposes a system that utilizes Optical Character
Recognition (OCR) and Natural Language
Processing (NLP) to automatically digitize and
interpret handwritten medical notes. By integrating
Tesseract OCR with Machine Learning
techniques—specifically Convolutional Neural
Networks (CNN) and Bidirectional Long Short-
Term Memory (BiLSTM) models—the system
effectively extracts textual content from complex
handwritten inputs. Subsequently, NLP techniques,
including Named Entity Recognition (NER) with
BiLSTM and Conditional Random Fields (CRF),
are employed to identify and structure critical
medical information such as patient names,
medications, dosages, and diagnoses. The proposed
solution significantly improves the accuracy,
efficiency, and automation of medical
documentation, enabling better healthcare record
management and reducing human error. Designed
with scalability and usability in mind, this system
represents a robust approach to modernizing
healthcare data processing through intelligent
document recognition and semantic analysis.