A Survey of Legal Text Analysis Techniques for Indian Legal Documents

Recent advancements in Natural Language Processing (NLP), particularly transformer-based models, have brought semantically rich word embeddings to the forefront. These innovations hold significant potential for revolutionizing the legal domain by enhancing the analysis and processing of legal documents, thus accelerating justice delivery. In India, with its extensive legal corpus and common law system, implementing NLP technologies presents both challenges and opportunities. The abundance of unstructured data complicates text analysis and information retrieval. This review paper examines the current research landscape, focusing on NLP applications in Indian court case documents. It critically explores progress in areas such as catchphrase identification, court judgment prediction, document summarization, rhetorical role labeling, similarity analysis of court case documents, and legal statute identification. Additionally, it discusses the development of existing language models retrained on Indian legal data to optimize judicial operations, reviewing datasets used and highlighting potential improvements and future research directions.