• Title/Summary/Keyword: 서류 검토 자동화

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Development of an Automated ESG Document Review System using Ensemble-Based OCR and RAG Technologies

  • Eun-Sil Choi
    • Journal of the Korea Society of Computer and Information
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    • v.29 no.9
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    • pp.25-37
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    • 2024
  • This study proposes a novel automation system that integrates Optical Character Recognition (OCR) and Retrieval-Augmented Generation (RAG) technologies to enhance the efficiency of the ESG (Environmental, Social, and Governance) document review process. The proposed system improves text recognition accuracy by applying an ensemble model-based image preprocessing algorithm and hybrid information extraction models in the OCR process. Additionally, the RAG pipeline optimizes information retrieval and answer generation reliability through the implementation of layout analysis algorithms, re-ranking algorithms, and ensemble retrievers. The system's performance was evaluated using certificate images from online portals and corporate internal regulations obtained from various sources, such as the company's websites. The results demonstrated an accuracy of 93.8% for certification reviews and 92.2% for company regulations reviews, indicating that the proposed system effectively supports human evaluators in the ESG assessment process.

Development of ITB Risk Mgt. Model Based on AI in Bidding Phase for Oversea EPC Projects (플랜트 EPC 해외 사업을 위한 입찰단계 시 AI 기반의 ITB Risk 관리 모델 개발)

  • Lee, Don-Hee;Yoon, Gun-Ho;Kim, Jeong-Joon
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.19 no.4
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    • pp.151-160
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    • 2019
  • EPC companies to continue operating overseas, it is increasingly becoming apparent that risk is no longer something to be avoided but a subject to be managed. During the bidding stage, the requirements, specifications and project line items within the bid package must be studied in details to analyze the various risk factors in order to avoid cost overruns. However, reviewing vast quantities of bidding documents is time consuming and labor intensive and is not an easy task and this is where automated information technology can help. For this study, I have constructed an ITB analysis model based on Watson AI that can analyze and apply vast amount of documents more effectively in a short time. Configuration of the Watson Explorer AI architecture for AI-based ITB risk management model research, the selection of learning procedures and analysis subjects, and the performance evaluation criteria were defined, and a test bed was constructed to conduct a pilot research. Consequently, I verified the effectiveness of the analytical time reduction and the quality of its results and VOC operations by professionals.