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

arXiv: https://arxiv.org/abs/2412.19840 · HTML: https://mlsanity.com/paper/2412.19840/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 9 references that are in the map.
- First posted on arXiv 24 Dec 2024.

## Builds on (most cited first, 1 of 1)

- [ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion Modeling](https://mlsanity.com/paper/2402.16194/) (2024)

## Built on by (most cited first, 6 of 6)

- [Arabic Large Language Models for Medical Text Generation](https://mlsanity.com/paper/2509.10095/) (2025)
- [Scaling Arabic Medical Chatbots Using Synthetic Data: Enhancing Generative AI with Synthetic Patient Records](https://mlsanity.com/paper/2509.10108/) (2025)
- [An Agentic Flow for Finite State Machine Extraction using Prompt Chaining](https://mlsanity.com/paper/2507.11222/) (2025)
- [MultiFuzz: A Dense Retrieval-based Multi-Agent System for Network Protocol Fuzzing](https://mlsanity.com/paper/2508.14300/) (2025)
- [Improving Answer Extraction in Context-based Question Answering Systems Using LLMs](https://mlsanity.com/paper/2606.06197/) (2026)
- [A Self Consistency Based Reranking for Narrative Question Answering](https://mlsanity.com/paper/2606.15741/) (2026)

## Abstract (arXiv)

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.