Automated Invoice Data Extraction: Using LLM and OCR

TL;DR

This paper presents a comprehensive AI platform that combines OCR, deep learning, LLMs, and graph analytics to significantly improve invoice data extraction accuracy and flexibility across diverse document layouts.

Contribution

It introduces an integrated AI system leveraging OCR, LLMs, and graph analytics, advancing invoice data extraction beyond existing hybrid approaches.

Findings

Higher extraction accuracy than traditional OCR methods

Enhanced contextual understanding with LLMs

Robust performance across varied invoice formats

Abstract

Conventional Optical Character Recognition (OCR) systems are challenged by variant invoice layouts, handwritten text, and low-quality scans, which are often caused by strong template dependencies that restrict their flexibility across different document structures and layouts. Newer solutions utilize advanced deep learning models such as Convolutional Neural Networks (CNN) as well as Transformers, and domain-specific models for better layout analysis and accuracy across various sections over varied document types. Large Language Models (LLMs) have revolutionized extraction pipelines at their core with sophisticated entity recognition and semantic comprehension to support complex contextual relationship mapping without direct programming specification. Visual Named Entity Recognition (NER) capabilities permit extraction from invoice images with greater contextual sensitivity and much higher accuracy rates than older approaches. Existing industry best practices utilize hybrid architectures that blend OCR technology and LLM for maximum scalability and minimal human intervention. This work introduces a holistic Artificial Intelligence (AI) platform combining OCR, deep learning, LLMs, and graph analytics to achieve unprecedented extraction quality and consistency.