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2.1  Medical Prescription Recognition using Machine Learning
Key Aspects:
The paper discusses the problem of reading doctors' handwritten prescriptions and proposes a solution using Convolutional Neural Networks (CNN). The goal is to develop a system that can accurately recognize and classify the medicines mentioned in the prescription. The paper mentions various related works in the field of handwritten recognition, including approaches like Optical Character Recognition (OCR) and hybrid whale Optimization Algorithm with neighborhood rough set.
The proposed model utilizes a dataset of handwritten medical prescriptions, which is divided into training and testing sets. The prescription is divided into three main parts: the doctor's name and specialization, the prescribed medicines, and the footer. The CNN is used as a feature extractor and classifier for the medicines in the prescription.The results and discussion section of the paper mentions that more handwritten medical prescriptions will be collected to improve the accuracy of the model. Additionally, other classification techniques like OCR will be applied to the medicines if the accuracy is low. The paper concludes by highlighting the benefits of the proposed model and the need for further research in this area.
Overall, the paper focuses on the problem of recognizing doctors' handwritten prescriptions and proposes a CNN-based solution. It discusses related works, describes the dataset used, and presents the results and future directions of the research
2.2 Handwriting Recognition for Medical Prescriptions using a CNN-Bi-LSTM Model 
Key Aspects:
This paper discusses the problem of incorrect reading of medical prescriptions and proposes a solution using deep learning techniques. The illegible handwriting of doctors and the lack of medical knowledge among patients and chemists often lead to errors in understanding and dispensing medications. The paper presents a model that utilizes deep learning architectures, such as LSTM and convolutional networks, to recognize handwritten text in medical prescriptions. The model is trained on a dataset of medical prescriptions and incorporates techniques like data augmentation and connectionist temporal classification to improve accuracy. The results show that the proposed technique can effectively recognize medical prescriptions and reduce errors in medication administration.
Overall, the paper highlights the importance of leveraging deep learning for text recognition in the medical domain. By addressing the issue of illegible prescriptions, the proposed model has the potential to improve patient safety and prevent harmful consequences. The combination of advanced neural network architectures and techniques like data augmentation and string matching algorithms contributes to the accuracy and reliability of the system. Further research and development in this area can lead to more efficient and accurate recognition of medical prescriptions, benefiting both healthcare providers and patients
2.3 Medical Handwritten Prescription Recognition and Information Retrieval using Neural Network
Key Aspects:
The paper discusses the use of computer technology to interpret handwritten medical prescriptions and retrieve information from them. It highlights the challenges of recognizing unconstrained handwriting due to its high variability. The authors propose a model based on Neural Networks to recognize medical handwritten prescriptions and retrieve the information. The system aims to improve the previous methods of image recognition for handwritten medical prescriptions.The paper emphasizes the relevance of this technology in the context of the Covid-19 pandemic. It mentions that offline doctor's prescription images are extensively being exchanged among patients and offline medical consultants for useful advice. The automatic interpretation of these prescription images can aid in the development of expert diagnostic systems, extraction of information from patients' history, detection of wrong medication, and analysis and comparison of prescribed medicines by doctors.
The proposed model utilizes the Extended MNIST dataset and demonstrates the efficiency of the system in identifying poor legibility of handwriting and transforming it into readable correct text recognition. The application areas where the proposed character recognition system can be utilized include recognizing medicine names from doctor's prescriptions, historical document recognition, automatic reading of bank's cheques, automatic postal code identification, converting handwriting in real-time, and extracting data from filled-in forms.
Overall, the paper highlights the potential of computer technology in interpreting handwritten medical prescriptions and retrieving information from them, with a focus on character recognition from connected handwriting. It emphasizes the relevance of this technology in the context of the Covid-19 pandemic and its applications in various healthcare scenarios
2.4 PatientCare: Patient Assistive Tool With Automatic Hand-written Prescription Reader
Key Aspects:
This paper presents a unique approach to medication recognition and prescription analysis using Optical Character Recognition (OCR) techniques, neural networks, and text classification. The research team aimed to achieve better results and provide users with a high-level user experience through the system. The study focuses on recognizing handwritten prescriptions and converting them into machine-encoded texts for storage and analysis. The process involves image segmentation, grayscale conversion, thresholding, and morphological transformations to enhance the readability of the prescription. The model trained on a new dataset showed high accuracy in recognizing medicine names, doses, and directions. The proposed system also includes features such as maintaining a medical history and analyzing blood reports to provide a comprehensive medical profile for users.
In summary, this paper introduces a novel approach to medication recognition and prescription analysis using OCR techniques and neural networks. The system aims to improve the accuracy and user experience in understanding and managing prescriptions. By utilizing image processing techniques, the model successfully recognizes medicine names, doses, and directions with a high level of accuracy. The system also includes features for storing medical history and analyzing blood reports, providing users with a comprehensive medical profile. This research contributes to the field of healthcare technology by offering an innovative solution for medication management and analysis.
2.5 AI Prescription Recognition System
Key Aspects:
The paper discusses an AI prescription recognition system designed to assist pharmacists in their work. The system consists of three main components: pre-processing, information extraction, and post-processing.In the pre-processing stage, the input image is enhanced for better contrast, followed by optical character recognition (OCR) to extract the text from the image. The extracted characters are then merged and sorted.
The information extraction component utilizes a hybrid approach, combining rule-based methods and deep learning techniques. Predefined keywords, rules, and databases are used for rule-based extraction, while the deep learning approach takes unmerged texts and bounding boxes as input tokens. The system achieves an average F1-score of 93.8% in the deep learning-based information.
The system also includes a user interface in the form of a web application server built with Django. Pharmacists can upload, edit, and manage prescriptions through this interface. Additionally, a client program is designed to monitor a designated folder for scanned prescriptions. The program automatically uploads the scanned prescriptions to the server and copies the recognition result for the pharmacist to use. Overall, the system aims to improve the efficiency of pharmacists by automating the prescription recognition process