Automated IPC Section Prediction using Hybrid Deep Learning and NLP Techniques

Dr.N.Sevugapandi, K.Ponraj

Indian Penal Code (IPC); First Information Report (FIR); Natural Language Processing (NLP); Bi-LSTM; RoBERTa; Hybrid Deep Learning; Text Classification; Legal Text Analysis; IPC Section Prediction; Transformer Model; Sequential Learning; Contextual Embeddings.

This paper presents an intelligent system for automatic prediction of Indian Penal Code (IPC) sections based on FIR (First Information Report) text. The system uses Natural Language Processing (NLP) techniques combined with a hybrid deep learning model consisting of Bi-LSTM and RoBERTa. The model analyzes case descriptions and predicts the most relevant IPC section along with confidence score. Additionally, the system provides automated legal explanation for better understanding. The proposed system improves accuracy compared to traditional machine learning models and helps in faster legal decision support. This solution can assist police departments, legal professionals, and judicial systems in reducing manual effort and improving efficiency. The legal domain involves complex analysis of textual data such as FIR (First Information Reports), which requires expertise and time to determine the appropriate IPC sections. This paper proposes an intelligent system that automates the prediction of IPC sections using Natural Language Processing (NLP) and a hybrid deep learning model combining Bi-LSTM and RoBERTa. The system processes unstructured legal text, extracts meaningful features, and predicts the most relevant IPC sections with a confidence score. Additionally, the system provides automatic IPC explanations from a structured database, improving interpretability. Experimental results demonstrate that the hybrid approach significantly improves prediction accuracy compared to traditional machine learning methods. The proposed system can assist law enforcement agencies, legal practitioners, and judicial systems in enhancing efficiency and reducing manual effort.

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