Introduction
Medical data extraction is crucial in healthcare, enabling digitization and analysis of patient information. Advancements in technology necessitate efficient methods, leading to the use of electronic medical databases. These databases offer benefits like improved disease treatments and clinical data for research.
Problem Statement
The problem revolves around accurately extracting patient details and advised investigation tests from medical documents like reports and prescriptions. The challenge lies in dealing with variations in document formats, handwriting, and image quality, which can lead to errors and inaccuracies in data extraction.
Project Description
The project aims to create an automated system for extracting patient details from medical documents using optical character recognition technology, reducing manual effort and improving data efficiency.
Methodology
- pdf2image:- Used to convert pdf to image.
- OpenCv:- Used for computer vision tasks such as image processing, object detection.
- Pytesseract:- Used for optical character recognition (OCR) tasks in Python, enabling the extraction of text from images or scanned documents.
- Regex (Regular expression):- Used for pattern matching and text manipulation tasks, allowing for sophisticated search and extraction operations based on specific patterns within strings or documents.
- File handling:- used for creating a file and for matching the text from file.
- fpdf:- Used to convert text into pdf.
- MySQL:- Used to store the data of patients.
Future Work
- Improvement in Accuracy
- Expansion to Other Languages
- Integration with EHR Systems
- Advanced Data Analysis
- User Interface Enhancements
- Scalability and Performance Optimization