Search Engine for Legal Documents and Pleaded Cases in Courts

Abstract

The increasing complexity of legal research and the demand for quick, efficient legal services, particularly in Intellectual Property Rights (IPR), require innovative solutions. This project proposes the design of an Artificial Intelligence (AI) and Natural Language Processing (NLP)-assisted specialized Legal Search Engine for the effective retrieval and classification of legal information, particularly Trademark and Copyright pleaded cases and ordinances. This initiative enhances and redefines legal research by incorporating deep learning for text classification, ontology models for relational mapping, and a user-friendly interface for accessibility. Using advanced models like the fine-tuned PAK-LEGAL-BERT, Naive Bayes, and XGBoost, this system achieves accurate case data extraction, ordinance mapping, and judgment forecasting. The system also integrates Flask for RESTful API development and React.js for an intuitive user interface. Additionally, an AI-powered chatbot, built using Naive Bayes and the Gemini API, enhances user engagement by delivering instant support and comprehensive case summaries.