Digitization of Document and Information Extraction using OCR

TL;DR

This paper introduces a hybrid framework combining OCR and Large Language Models to improve document text extraction, accuracy, and semantic understanding across various formats.

Contribution

It presents a novel integrated approach that merges OCR with LLMs for enhanced structured data extraction from diverse document types.

Findings

Significant accuracy improvements over traditional methods

Effective handling of ambiguous and complex layouts

Enhanced semantic understanding in extracted data

Abstract

Retrieving accurate details from documents is a crucial task, especially when handling a combination of scanned images and native digital formats. This document presents a combined framework for text extraction that merges Optical Character Recognition (OCR) techniques with Large Language Models (LLMs) to deliver structured outputs enriched by contextual understanding and confidence indicators. Scanned files are processed using OCR engines, while digital files are interpreted through layout-aware libraries. The extracted raw text is subsequently analyzed by an LLM to identify key-value pairs and resolve ambiguities. A comparative analysis of different OCR tools is presented to evaluate their effectiveness concerning accuracy, layout recognition, and processing speed. The approach demonstrates significant improvements over traditional rule-based and template-based methods, offering enhanced flexibility and semantic precision across different document categories