Legal documents Text Analysis using Natural Language Processing (NLP)

India’s judiciary system faces a significant backlog of cases, leading to delays in justice delivery. This research explores the potential of Natural Language Processing (NLP) techniques to streamline legal processes and improve efficiency. By utilizing NLP capabilities such as information retrieval, encoding, sentiment analysis, and Optical Character Recognition (OCR), the proposed solution aims to automate the analysis of legal documents, reducing the workload on judges and lawyers. The study focuses on the application of topic modeling using Latent Dirichlet Allocation (LDA) and text summarization using Transformers to extract key information and generate concise summaries of legal documents. Additionally, text KPIs visualization techniques can be employed to gain valuable insights from legal data. By integrating NLP into the legal system, it is anticipated that the efficiency of case processing can be significantly improved, leading to faster resolution of cases and a more equitable justice system.