ERPA: Efficient RPA Model Integrating OCR and LLMs for Intelligent Document Processing
This addresses performance limitations in RPA for immigration document processing, offering a faster and more reliable alternative, though it appears incremental as it builds on existing OCR and LLM technologies.
The paper tackles the problem of inefficient document processing in Robotic Process Automation (RPA) for immigration workflows by proposing ERPA, which integrates OCR and LLMs to improve text extraction accuracy and clarity. The result is a significant reduction in processing times by up to 94%, completing ID data extraction in 9.94 seconds.
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.