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Overview
This project leverages advanced image processing techniques to extract text from multiple PDF files and generate customizable tables with a cell structure. The intuitive interface allows users to effortlessly add, delete, or modify cells and their structures, tailored for seamless data organization. The designed border-less cell structure ensures smooth data transfer. Users can export cells to Excel or JSON format for further analysis. The aim is to offer an intuitive solution for efficient data management from PDFs.
Challenges
Complex PDF Data Extraction:
- Extracting structured data from PDFs, particularly tables, presents challenges due to varied formatting and layouts.
Manual Data Entry:
- Manual transcription of data from PDF tables is time-consuming and prone to errors, hindering efficiency.
OCR Inaccuracies:
- OCR engines like Tesseract and Paddle OCR struggle with accuracy for complex layouts and fonts.
Solution
Advanced Image Processing:
- Employ OpenCV for image preprocessing, enhancing data extraction accuracy from PDFs.
Web Interface for Custom Tables:
- Develop a Python-Flask web app allowing easy modification of table cells and structures.
Enhanced OCR Engine Usage:
- Leverage Tesseract, Paddle OCR, and Easy OCR, optimizing accuracy through training and customization.
Development Process
Research
Planning
Designing
Development
Maintenance
Tools/Technologies
