A Deep Learning Based Framework for AI Assisted Judicial Decision Support through Legal Document Analysis

The increasing volume of digital court records has made judicial case analysis more complex and time-consuming. This study proposes an Artificial Intelligence (AI) assisted judicial decision support framework based on deep learning to support judges in analyzing legal documents efficiently. The proposed system assists in the analysis of criminal, civil, family, property, commercial, labour, cybercrime, banking, constitutional, consumer, environmental, medical negligence, motor accident, and other judicial cases. AI is designed solely as a decision support technology, while the final legal interpretation and judgment remain exclusively under the authority of the judge. The proposed framework consists of three stages: data preprocessing, deep learning-based analysis, and performance evaluation. During preprocessing, legal documents including case history, facts, petitioner and respondent evidence, witness statements, judicial notes, previous judgments, legal precedents, and statutory provisions are cleaned and processed using a Median Filter to improve data quality. The preprocessed data are analyzed using Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) models to classify legal documents, retrieve relevant precedents, summarize evidence, identify applicable legal provisions, and generate structured judicial analysis reports. The performance of both models is evaluated using accuracy, precision, recall, F1 score, and processing time to determine the most effective approach.

The proposed AI framework improves the efficiency and consistency of legal document analysis, reduces manual workload, and supports faster judicial case review. The generated analytical reports assist judges in understanding complex legal information while preserving judicial independence. The study demonstrates that deep learning can serve as an effective and reliable technology for intelligent judicial decision support without replacing the role of judges.