Machine Learning for PDF Image Extraction

Idea in a Nutshell

Extracting images from PDFs is a tedious task when done manually. This project explores how Machine Learning can automate image extraction and cropping within PDFs, streamlining workflows for researchers, publishers, and analysts.

Research Objectives

• Develop an ML pipeline that detects and extracts images embedded in PDFs.
• Enhance cropping mechanisms using object detection to remove unnecessary whitespace.
• Optimize extracted images for clarity and format consistency.
• Test performance across different document types (scientific papers, scanned documents, books).

Methodology

The process involves multiple steps to ensure high-quality image extraction:

• Use **OpenCV** and **Tesseract OCR** to identify image regions within PDFs.
• Apply **Deep Learning-based Object Detection** (YOLO, Faster R-CNN) to refine image boundaries.
• Use **image segmentation** techniques to enhance cropping precision.
• Convert images to appropriate formats (JPEG, PNG) with minimal quality loss.
• Develop a user-friendly interface for uploading and processing PDFs.

Potential Applications

• Automating figure and diagram extraction for research papers.
• Improving document digitization workflows for archival purposes.
• Assisting media and content creators in extracting visuals from reports.
• Enabling AI-driven data visualization by extracting charts and graphs.