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.

翻译：本文提出ERPA，一种创新的机器人流程自动化（RPA）模型，旨在提升移民工作流程中的身份数据提取效率并优化光学字符识别（OCR）任务。传统RPA解决方案在处理大规模文档时常常面临性能限制，导致效率低下。ERPA通过集成大型语言模型（LLM）来提升提取文本的准确性与清晰度，有效处理模糊字符与复杂文档结构，从而应对这些挑战。与UiPath和Automation Anywhere等主流平台的基准对比表明，ERPA将处理时间显著缩短了高达94%，仅需9.94秒即可完成身份数据提取。这些发现凸显了ERPA在革新文档自动化方面的潜力，为现有RPA解决方案提供了更快速、更可靠的替代方案。