Retrieval-Augmented Generation for Intelligent Question Answering from OCR-Processed PDFs

This research explores the application of Retrieval-Augmented Generation (RAG) for enhancing informationextraction and question-answering tasks from scanned PDF documents using Optical Character Recognition (OCR). Byintegrating a retrieval mechanism with a generative language model, we present a novel framework that intelligently interpretsnoisy, unstructured OCR outputs and enables contextual interaction via natural language queries[1][2]. The approach bridgesthe gap between image-based document archives and intelligent systems, facilitating improved document accessibility in fieldslike legal, academic, and archival research.