Authors :
Samson S; Tejaswini C; G Rishikesh; Ramu M; Ramesh T
Volume/Issue :
Volume 9 - 2024, Issue 1 - January
Google Scholar :
http://tinyurl.com/2p8kx67v
Scribd :
http://tinyurl.com/yp6db44y
DOI :
https://doi.org/10.5281/zenodo.10527101
Abstract :
This project proposes an end-to-end solution
for the automatic detection and extraction of medication
names from handwritten medical prescriptions by
doctors. It does this by combining computer vision and
deep learning techniques. The system consists of two
primary components: a YOLOv5-based medication
identification model that locates and crops drug names
from prescription photographs, and a deep learning text
recognition (OCR) model that extracts textual
information from the cropped medicine name areas.
Keywords :
Computer Vision, Machine Learning Model Based on Yolov5 for Medicine Name Extraction and Recognition Utilizing Optical Character Recognition (OCR).
This project proposes an end-to-end solution
for the automatic detection and extraction of medication
names from handwritten medical prescriptions by
doctors. It does this by combining computer vision and
deep learning techniques. The system consists of two
primary components: a YOLOv5-based medication
identification model that locates and crops drug names
from prescription photographs, and a deep learning text
recognition (OCR) model that extracts textual
information from the cropped medicine name areas.
Keywords :
Computer Vision, Machine Learning Model Based on Yolov5 for Medicine Name Extraction and Recognition Utilizing Optical Character Recognition (OCR).
