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Paper ID:IJSREM63657

AI Legal Simplifier: An Intelligent Multilingual Legal Document Analysis and Simplification System Using NLP and Web Technologies

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM63657

Volume:

10 |

5 |

May 2026

Pages:

1-3

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM63657

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

Gurpreet Kaur

Gurpreet Kaur

Computer Science and Engineering

Shri Rawatpura Sarkar University, Raipur, Chhattisgarh, India

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Payal Chandrakar

Computer Science and Engineering

Shri Rawatpura Sarkar University, Raipur

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Abstract

Legal documents are characterized by dense, jargon-laden language that creates significant comprehension barriers for non-expert users. This paper presents the AI Legal Simplifier (Lexi), a full-stack intelligent web application that automates the analysis, simplification, and explanation of legal documents using Natural Language Processing (NLP), Optical Character Recognition (OCR), and multilingual machine translation. The system accepts PDF, DOCX, and TXT inputs and produces structured plain-language summaries, critical clause extractions, and a quantified risk score (0–100) through a keyword-weighted TF-IDF sentence ranking algorithm. An interactive conversational chatbot enables context-aware document Q&A with multi-turn history and intelligent fallback to OpenAI GPT-3.5. Multilingual output in eight languages — including a custom Hinglish romanisation pipeline — makes legal information accessible to diverse linguistic communities. Additional contributions include server-side TTS/STT voice interface, SHA-256-secured revocable public share links, multilingual ReportLab PDF report generation with Unicode font support, and a side-by-side original-versus-simplified comparison view. The system is engineered on a Flask Blueprint service-layer architecture, uses SQLAlchemy ORM with bcrypt authentication, and is validated by a Hypothesis property-based test suite. Experimental results demonstrate sub-two-second analysis latency, accurate clause and risk detection across diverse legal document types, and production-grade security posture, making Lexi a practically deployable and academically significant contribution to the legal-tech domain.

KEYWORDS

Keywords — Natural Language Processing Legal Document Analysis Text Summarization TF-IDF Risk Scoring Multilingual Translation Hinglish Romanisation Chatbot OCR Flask Blueprint SQLAlchemy Cryptographic Token Property-Based Testing Legal-Tech

Cite This Article

Gurpreet Kaur, Payal Chandrakar, "AI Legal Simplifier: An Intelligent Multilingual Legal Document Analysis and Simplification System Using NLP and Web Technologies," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-3, May 2026. https://doi.org/10.55041/IJSREM63657

Full Article/Conclusion

This paper presented the AI Legal Simplifier (Lexi), a comprehensively engineered intelligent web system for legal document analysis. Lexi demonstrates that a practically deployable, secure, and multilingual legal document analysis system can be constructed without depending on large neural models for its primary functionality, using targeted NLP engineering to achieve results competitive with model-based approaches at a fraction of the inference cost. The system's sub-second analysis latency, 0.874 macro-F1 clause detection, 90% risk-score agreement with expert assessments, and production-grade security architecture position it as a significant contribution to democratizing access to legal document intelligence, particularly for multilingual communities in India and beyond

References

[1] L. Böhme and T. Moore, "The Iterated Weakest Link: A Model of Adaptive Security Investment," IEEE J. Sel. Topics Signal Process., vol. 7, no. 1, pp. 79–88, 2013. [2] J. Devlin, M. Chang, K. Lee, and K. Toutanova, "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding," in Proc. NAACL-HLT, Minneapolis, MN, 2019, pp. 4171–4186. [3]   Y. Liu et al., "RoBERTa: A Robustly Optimized BERT Pretraining Approach," arXiv:1907.11692, 2019. [4] I. Chalkidis et al., "LEGAL-BERT: The Muppets Straight Out of Law School," in Findings of EMNLP, 2020, pp. 2898–2904. [5]    Y. Koreeda and C. D. Manning, "ContractNLI: A Dataset for Document-Level NLI for Contracts," in Findings of EMNLP, 2021, pp. 1248–1264. [6] H. Zhong et al., "Legal Judgment Prediction via Topological Learning," in Proc. EMNLP, 2018, pp. 3540–3549. [7] C. Grover et al., "Automatic Summarization of Legal Documents," in Proc. Workshop on Legal Text Analysis, Geneva, 2003. [8] V. Sanh et al., "DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter," arXiv:1910.01108, 2019. [9] D. Hendrycks et al., "CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review," in Proc. NeurIPS Datasets & Benchmarks, 2021. [10]  A. Conneau et al., "Unsupervised Cross-lingual Representation Learning at Scale," in Proc. ACL, 2020, pp. 8440–8451.

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Publication Date

5/21/2026

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Review Rounds: 2

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