Abstract:
Doctors frequently write prescriptions in unreadable handwriting due to
the growing demands on healthcare workers, making it difficult to
correctly identify the names of the recommended medications. Patients are
greatly affected by this problem since they could find it difficult to
comprehend the prescription drugs they are meant to take. Because
doctors' handwriting styles vary so much, no method has been able to
completely address the challenge of recognizing handwritten medicine
names despite multiple tries. In this work, we present a solution that uses
machine learning techniques to identify handwritten pharmaceutical
names. The system is implemented through a mobile application that
captures prescription medicine images, preprocesses them with
techniques such as image crop, and resizing, gray scaling, normalization
and then classifies the images using a Convolutional Neural Network
(CNN). The proposed system is evaluated using a dataset of handwritten
medicine names, with the CNN model demonstrating an accuracy of
83.53%. By reducing medicine name misinterpretations, this technology
helps patients and pharmacists ensure proper prescription consumption.
