Legal AI: An Intelligent Legal Document Simplification Using NLP

References

[1] Quevedo E, Cerny T, Rodriguez A, Rivas P, Yero J, Sooksatra K, et al. Legal natural language processing from 2015 to 2022: A comprehensive systematic mapping study of advances and applications. IEEE Access. 2024;12:145286–145317. https://doi.org/10.1109/ACCESS.2023.3333946

[2] Tekade PM, Jadhav N, Chawda S, Bhosale V, Akashe Y. Legal Lens: Exploring NLP for document analysis in law. In: Proc. 2024 IEEE Int. Conf. Blockchain and Distributed Systems Security (ICBDS). 2024. p. 1–5. https://doi.org/10.1109/ICBDS61829.2024.10837237

[3] Elsayed M, Hassan A, Salah M. Enhancing legal document understanding and analysis using retriever-augmented generation (RAG). In: Proc. 2025 Int. Conf. Intelligent Methods, Systems and Applications (IMSA). 2025.

[4] Siino M, Falco M, Croce D, Rosso P. Exploring LLMs applications in law: A literature review on current legal NLP approaches. IEEE Access. 2025;13:18253–18276. https://doi.org/10.1109/ACCESS.2025.3533217

[5] Niklaus J, Merane J, Nenadic L, Ahmadi S, Gao Y, Chevalley CAH, et al. SwiLTra-Bench: The Swiss legal translation benchmark. In: Proc. 63rd Annual Meeting of the Association for Computational Linguistics (ACL). 2025. p. 14894–14916. https://doi.org/10.18653/v1/2025.acl-long.725

[6] Huber-Fliflet N, Zhang J, Gronvall P, Wei F, Spinelli P. Experimental study of in-context learning for text classification and its application to legal document review in construction delay disputes. In: Proc. 2024 IEEE Int. Conf. Big Data (BigData). 2024. p. 2119–2125. https://doi.org/10.1109/BigData62323.2024.10826061

[7] Lim EY, Park J. Agentic RAG for legal question answering in civil law: Evidence from the Korean bar examination. IEEE Access. 2026;14:124441–124458. https://doi.org/10.1109/ACCESS.2026.3722717

[8] Sriram S, Vijayaraj N, Rajiv Krishna G, Vijay Bhanu S, Choudhary G, Murugan T. A reusable prompting framework for applying large language models to legal tasks. IEEE Access. 2026;14:3108–3129. https://doi.org/10.1109/ACCESS.2026.3650941

[9] Paul A, Gupta AB, Bhat SY, Kumar PGS. Pre-training transformers on Indian legal text. In: Proc. 2025 Joint Int. Conf. on Computational Linguistics, Language Resources and Evaluation (LREC-COLING). 2025.

[10] Fan A, Wang S, Wang Y. Legal document similarity matching based on ensemble learning. IEEE Access. 2024;12:33910–33922. https://doi.org/10.1109/ACCESS.2024.3371262

[11] Smith R. An overview of the Tesseract OCR engine. In: Proc. Ninth Int. Conf. Document Analysis and Recognition (ICDAR). 2007. p. 629–633. https://doi.org/10.1109/ICDAR.2007.4376991

[12] Pallets. Flask documentation (Flask 3.1.x). 2026.

[13] Oracle Corporation. MySQL 8.0 reference manual. Oracle Corporation; 2026.

[14] Python Software Foundation. Python 3.11 documentation. Python Software Foundation; 2026.

[15] WHATWG. HTML living standard. Web Hypertext Application Technology Working Group; 2026.

@article{1724256,
      author = {Dr. Dhanya K N, Akshay Kumar R, Bhoomika L S, Divyanethri P, Kiran S S},
      title = {Legal AI: An Intelligent Legal Document Simplification Using NLP},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {4},
      pages = {3679-3689},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1724256.pdf},
      abstract = {Legal documents such as rental agreements, employment contracts, service agreements, and policy documents often contain complex legal terminology, lengthy clauses, and technical language that make them difficult for non-legal users to understand. Identifying important conditions related to notice periods, penalties, and payment obligations requires considerable time and effort. To address these challenges, this paper presents Legal AI, an intelligent legal document simplification system using Natural Language Processing (NLP). The proposed system supports multiple document formats, including PDF, DOCX, TXT, images, and scanned documents, and uses Optical Character Recognition (OCR) for extracting text from scanned documents. The extracted content is processed to identify important legal clauses, generate simplified summaries, highlight potential risks, extract deadlines, analyze financial obligations, and provide document-based question answering through an AI assistant. Retrieval-Augmented Generation (RAG) is incorporated to support responses grounded in the uploaded document. The system is implemented as a web-based application using Python, Flask, MySQL, HTML, CSS, and JavaScript. The proposed platform aims to provide an accessible preliminary document-understanding tool for students, professionals, and general users by presenting complex legal information in a simplified and interactive form. The system is intended for preliminary document understanding and does not replace professional legal advice.},
      keywords = {Legal Document Simplification; Natural Language Processing; Optical Character Recognition; Retrieval-Augmented Generation; Clause Detection; Risk Analysis; AI Chatbot},
      month = {October},
  }