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In an insightful investigation entitled “Medical Prescription Recognition Using Machine Learning,” researchers
about the challenge of deciphering doctors’ handwritten prescriptions through the lens of Convolutional Neural
Networks (CNN). This study meticulously reviews existing methodologies in handwritten recognition, highlighting
prior advancements such as Optical Character Recognition (OCR) and the innovative hybrid whale Optimization
Algorithm with a neighborhood rough set, setting a foundational framework for the proposed solution. The researchers
embark on a detailed analysis using a segmented dataset of handwritten medical prescriptions, meticulously evaluating
each segment for accurate classification by the CNN. The future trajectory of this research envisages the integration
of a broader dataset and the exploration of OCR techniques to enhance classification accuracy, pointing towards a
promising direction in improving medical prescription recognition through machine learning technologies.
“Handwriting Recognition for Medical Prescriptions using a CNN-Bi-LSTM Model” presents a groundbreaking
approach to resolving the prevalent issue of misreading medical prescriptions due to poorly legible handwriting.
Employing a combination of Long Short-Term Memory (LSTM) models and convolutional networks, this paper
outlines the development of a deep learning model trained on an extensive dataset of medical prescriptions.
Emphasizing the model’s adeptness in reducing medication dispensation errors, the paper advocates for the application
of deep learning techniques in medical text recognition to safeguard patient well-being. By utilizing advanced neural
network architectures alongside methods such as data augmentation and temporal classification, the study showcases
the potential of deep learning in significantly improving the accuracy of prescription recognition and, by extension,
enhancing patient safety in the medical domain.
The paper titled “Medical Handwritten Prescription Recognition and Information Retrieval using Neural Network”
focuses on leveraging computer technology to interpret hand-written medical prescriptions, an endeavor made
particularly relevant in the context of the Covid-19 pandemic. The proposed neural network-based model aims to
overcome the challenges presented by the high variability of unconstrained handwriting, offering a viable solution for
recognizing and retrieving information from handwritten prescriptions. By utilizing the Extended MNIST dataset, the
model demonstrates unprecedented efficiency in transforming illegible handwriting into intelligible text. This study
not only underscores the applicability of this technology across various healthcare scenarios but also emphasizes its
potential in aiding the development of expert diagnostic systems and enhancing the accuracy of medication extraction
from patient histories.
In “PatientCare: Patient Assistive Tool With Automatic Hand-written Prescription Reader,” researchers introduce
an innovative approach to medication recognition and prescription analysis using Optical Character Recognition
(OCR) techniques backed by neural networks. This study explores the transformation of handwritten prescriptions
into machine-encoded texts through advanced image processing techniques, achieving high accuracy in recognizing
medication details. The system also extends its capabilities to maintaining medical histories and analyzing blood
reports, offering users a holistic medical profile management tool. This paper marks a significant contribution to
healthcare technology, heralding a new era of efficient and accurate medication management and analysis.