ERPA: Efficient RPA Model Integrating OCR and LLMs for Intelligent Document Processing
- URL: http://arxiv.org/abs/2412.19840v1
- Date: Tue, 24 Dec 2024 09:44:43 GMT
- Title: ERPA: Efficient RPA Model Integrating OCR and LLMs for Intelligent Document Processing
- Authors: Osama Abdellaif, Abdelrahman Nader, Ali Hamdi,
- Abstract summary: 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.
Benchmark comparisons demonstrate that ERPA significantly reduces processing times by up to 94 percent, completing ID data extraction in just 9.94 seconds.
- Score: 0.0
- License:
- 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.
Related papers
- Adaptive Data Exploitation in Deep Reinforcement Learning [50.53705050673944]
We introduce ADEPT, a powerful framework to enhance the **data efficiency** and **generalization** in deep reinforcement learning (RL)
Specifically, ADEPT adaptively manages the use of sampled data across different learning stages via multi-armed bandit (MAB) algorithms.
We test ADEPT on benchmarks including Procgen, MiniGrid, and PyBullet.
arXiv Detail & Related papers (2025-01-22T04:01:17Z) - LMRPA: Large Language Model-Driven Efficient Robotic Process Automation for OCR [0.0]
This paper introduces LMRPA, a novel Large Model-Driven Robotic Process Automation model.
It is designed to greatly improve the efficiency and speed of Optical Character Recognition (OCR) tasks.
arXiv Detail & Related papers (2024-12-24T00:21:36Z) - LMV-RPA: Large Model Voting-based Robotic Process Automation [0.0]
This paper introduces LMV-RPA, a Large Model Voting-based Robotic Process Automation system to enhance OCR.
LMV-RPA integrates outputs from OCR engines such as Paddle OCR, Tesseract OCR, Easy OCR, and DocTR with Large Language Models.
It achieves 99 percent accuracy in OCR tasks, surpassing baseline models with 94 percent, while reducing processing time by 80 percent.
arXiv Detail & Related papers (2024-12-23T20:28:22Z) - Optimizing Structured Data Processing through Robotic Process Automation [2.3997896447030653]
This study investigates the use of RPA for structured data extraction and evaluates its advantages over manual processes.
By comparing human-performed tasks with those executed by RPA software bots, we assess efficiency and accuracy in data extraction from invoices.
Our findings highlight the significant efficiency gains achieved by RPA, with bots completing tasks in significantly less time compared to manual efforts.
arXiv Detail & Related papers (2024-08-27T05:53:02Z) - Automatic AI Model Selection for Wireless Systems: Online Learning via Digital Twinning [50.332027356848094]
AI-based applications are deployed at intelligent controllers to carry out functionalities like scheduling or power control.
The mapping between context and AI model parameters is ideally done in a zero-shot fashion.
This paper introduces a general methodology for the online optimization of AMS mappings.
arXiv Detail & Related papers (2024-06-22T11:17:50Z) - APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking [39.649879274238856]
We introduce a novel automatic prompt engineering algorithm named APEER.
APEER iteratively generates refined prompts through feedback and preference optimization.
Experiments demonstrate the substantial performance improvement of APEER over existing state-of-the-art (SoTA) manual prompts.
arXiv Detail & Related papers (2024-06-20T16:11:45Z) - Superposition Prompting: Improving and Accelerating Retrieval-Augmented Generation [22.124234811959532]
Large language models (LLMs) exhibit significant drawbacks when processing long contexts.
We propose a novel RAG prompting methodology, which can be directly applied to pre-trained transformer-based LLMs.
We demonstrate the capability of our method to simultaneously enhance time efficiency across a variety of question-answering benchmarks.
arXiv Detail & Related papers (2024-04-10T11:03:17Z) - Reinforced In-Context Black-Box Optimization [64.25546325063272]
RIBBO is a method to reinforce-learn a BBO algorithm from offline data in an end-to-end fashion.
RIBBO employs expressive sequence models to learn the optimization histories produced by multiple behavior algorithms and tasks.
Central to our method is to augment the optimization histories with textitregret-to-go tokens, which are designed to represent the performance of an algorithm based on cumulative regret over the future part of the histories.
arXiv Detail & Related papers (2024-02-27T11:32:14Z) - SERL: A Software Suite for Sample-Efficient Robotic Reinforcement
Learning [85.21378553454672]
We develop a library containing a sample efficient off-policy deep RL method, together with methods for computing rewards and resetting the environment.
We find that our implementation can achieve very efficient learning, acquiring policies for PCB board assembly, cable routing, and object relocation.
These policies achieve perfect or near-perfect success rates, extreme robustness even under perturbations, and exhibit emergent robustness recovery and correction behaviors.
arXiv Detail & Related papers (2024-01-29T10:01:10Z) - Automatic Engineering of Long Prompts [79.66066613717703]
Large language models (LLMs) have demonstrated remarkable capabilities in solving complex open-domain tasks.
This paper investigates the performance of greedy algorithms and genetic algorithms for automatic long prompt engineering.
Our results show that the proposed automatic long prompt engineering algorithm achieves an average of 9.2% accuracy gain on eight tasks in Big Bench Hard.
arXiv Detail & Related papers (2023-11-16T07:42:46Z) - Accelerating Robotic Reinforcement Learning via Parameterized Action
Primitives [92.0321404272942]
Reinforcement learning can be used to build general-purpose robotic systems.
However, training RL agents to solve robotics tasks still remains challenging.
In this work, we manually specify a library of robot action primitives (RAPS), parameterized with arguments that are learned by an RL policy.
We find that our simple change to the action interface substantially improves both the learning efficiency and task performance.
arXiv Detail & Related papers (2021-10-28T17:59:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.