CourtSense for Case Analysis: Legal Document Classification and Analysis using NLP and Deep Learning

- Legal documents are often lengthy, complex, and difficult to analyze manually due to their unstructured nature and technical language. This paper presents CourtSense for Case Analysis, an intelligent legal document analysis system developed using Natural Language Processing (NLP) and deep learning techniques. The proposed system is designed to automate important legal tasks such as document summarization, case outcome prediction, and similar case retrieval. The system accepts legal content in the form of PDF files, direct text input, or web URLs. Extracted text is preprocessed using cleaning and normalization techniques to remove unnecessary patterns and improve text quality. Since transformer-based models have token limitations, a chunking approach similar to a sliding window mechanism is used to process long legal documents without losing contextual information. The T5 transformer model is utilized to generate concise summaries, while a fine-tuned LegalBERT model is used for predicting legal outcomes with confidence scores. The system also identifies key legal issues and jurisdiction-related information from the input text. In addition, CourtSense provides similar case suggestions using a search-based retrieval mechanism, with future scope for semantic retrieval using dense vector embeddings. The application is implemented using Flask and PyTorch, providing a scalable and user-friendly interface for legal analysis. Experimental evaluation can be performed using ROUGE and METEOR metrics for summarization, and Precision, Recall, and F1-score for prediction tasks. The proposed system aims to reduce manual effort, improve efficiency, and support intelligent decision-making in legal workflows through automated document understanding and analysis.