Multi-Modal Retrieval-Augmented Generation (RAG) with Text and Image Processing

This project is designed to accelerate literature reviews and data-driven insights, it empowers researchers to focus on innovation rather than manual processing.

• Extracts text, tables, and images from research papers for faster analysis.

• Generates AI-powered summaries to quickly understand key findings.

• Enables semantic search for efficient research retrieval.

• Uses OCR to digitize scanned and handwritten research papers.

• Provides AI-driven explanations for academic figures and graphs.

• Converts tables into structured Pandas DataFrames for easy analysis.

• Answers research queries instantly using AI-powered retrieval.

• Automates literature reviews, saving time for researchers.

• Supports cross-paper analysis to compare methodologies and findings.

• Enhances AI-driven academic tools for smarter research workflows.