Doctors’ Handwriting Recognition

Abstract: Handwritten medical prescriptions, often written in illegible cursive or abbreviated forms by doctors, pose a significant challenge to accurate medication dispensing. Misinterpretation of such prescriptions can lead to medication errors, adversely affecting patient safety. The goal of this work is to create an intelligent system that can identify handwritten prescriptions using advanced deep learning techniques. The proposed tool allows users to upload an image of a prescription, which is then processed and converted into a structured, machine-readable format. The system accurately detects and interprets handwriting patterns frequently observed in medical prescriptions through the use of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks. By enhancing the readability of prescriptions, the system improves communication between healthcare providers, pharmacists, and patients. The successful implementation of this model has the potential to significantly reduce medication errors, improve prescription accessibility, and enhance overall efficiency in the healthcare delivery process.

Keywords: Handwriting recognition, deep learning, CNN, optical character recognition (OCR)