ERPA: Efficient RPA Model Integrating OCR and LLMs for Intelligent Document Processing

TL;DR

ERPA is a novel RPA model that integrates OCR and LLMs to significantly improve ID data extraction efficiency and accuracy in immigration workflows, outperforming existing platforms in speed and reliability.

Contribution

ERPA introduces a new approach combining OCR and LLMs within RPA to enhance document processing, reducing processing times and improving data extraction accuracy.

Findings

ERPA reduces processing time by up to 94%.

ERPA completes ID data extraction in approximately 9.94 seconds.

ERPA outperforms platforms like UiPath and Automation Anywhere.

Abstract

This paper presents ERPA, an innovative Robotic Process Automation (RPA) model designed to enhance ID data extraction and optimize Optical Character Recognition (OCR) tasks within immigration workflows. Traditional RPA solutions often face performance limitations when processing large volumes of documents, leading to inefficiencies. ERPA addresses these challenges by incorporating Large Language Models (LLMs) to improve the accuracy and clarity of extracted text, effectively handling ambiguous characters and complex structures. Benchmark comparisons with leading platforms like UiPath and Automation Anywhere demonstrate that ERPA significantly reduces processing times by up to 94 percent, completing ID data extraction in just 9.94 seconds. These findings highlight ERPA's potential to revolutionize document automation, offering a faster and more reliable alternative to current RPA solutions.