CourtSense: Leveraging NLP and Deep Learning for Legal Judgment Prediction

The Indian judiciary presently bears an unprecedented backlog of more than five crore pending cases, causing huge delays in dispensing justice. Manually analyzing cases and estimating outcomes by legal professionals are onerous, inconsistent, and subject to cognitive bias. This paper proposes an "AI-Assisted Legal Judgment Prediction System" for litigants, lawyers, and judges to obtain data-driven predictions of case outcomes from historical judgments and structured legal text analysis. The system collects data from public repositories automatically (Indian Kanoon, etc.), pre-processes, anonymizes, etc., and runs NLP pipelines for feature extraction, including named entities, cited statutes, fact patterns, precedent references, and others. For building the predictive model, we consider algorithms of all sorts — from classical ones such as Logistic Regression and SVM, to deep neural architectures such as CNN and BiLSTM, up to transformer-based models like BERT—with hyperparameter tuning guided by the Texas Wolf Optimization (TWO) heuristic. Models are trained and validated through k-fold cross-validation on datasets stored in efficient formats (Parquet/JSON), while performance is assessed based on accuracy, precision, recall, and F1-measure. Experimental results show that context-aware transformers (BERT) along with sequence models (BiLSTM) became superior-performing models for legal text classification, and TWO-based tuning provides some improvements in generalization. We then discuss the implications in legal decision-support, potential fairness and explainability concerns, data privacy guarantees, and directions for its responsible deployment in the Indian judicial context.