Smart IPC Section Prediction Using Machine Learning

Abstract

The increasing demand for faster, more consistent, and accessible legal analysis has driven the adoption of intelligent systems within the judicial domain. This paper presents a machine learning-based framework that predicts relevant sections of the Indian Penal Code (IPC) based on textual descriptions of criminal incidents. By utilizing natural language processing (NLP) techniques, the proposed system is capable of comprehending the context of a legal case, identifying significant legal cues, and mapping them to the appropriate IPC sections. The model is trained on a diverse dataset consisting of real and synthesized case summaries, enabling it to effectively learn the linguistic patterns and legal terminology used in criminal law. The primary objective of this work is to assist legal professionals, law enforcement agencies, and other stakeholders by providing quick, consistent, and reliable legal references during the initial evaluation of a case. This system aims to reduce the burden of manual analysis, minimize errors arising from subjective interpretation, and improve overall efficiency in the legal process. Furthermore, the project highlights the broader role of artificial intelligence (AI) in modernizing legal workflows and enhancing access to justice through data-driven insights. The results demonstrate the potential of predictive systems to transform legal practices and contribute to the development of smarter legal tools in the Indian judicial context. Through the use of state-of-the-art machine learning models such as DistilBERT and TinyBERT, this work provides a robust and scalable solution to automate the classification of case descriptions into relevant IPC sections, showcasing the utility of NLP in legal applications.

Introduction

Legal Text Classification Problem
Legal documents are abundant and critical for judicial decision-making, but manual classification is slow and error-prone. Automating this classification is essential to streamline legal workflows.

Importance of Automation in Legal Case Analysis
With increasing legal data volumes, automated systems help speed up access to references, improve case management, support predictive analysis, and reduce human bias.

Motivation for Machine Learning (ML) Use
Machine learning, especially NLP, is effective in understanding complex legal language patterns and can automate classification tasks like predicting Indian Penal Code (IPC) sections from case descriptions.

Study Objectives
This study compares two transformer-based models, DistilBERT and TinyBERT, for classifying legal case descriptions into IPC sections, focusing on accuracy, generalization, and performance.

Literature Review

• Legal text classification requires deep understanding of legal terminology and context.

• NLP techniques like tokenization and named entity recognition help extract meaningful features from legal texts.

• Transformer models (BERT variants) outperform traditional ML methods in legal text tasks.

• Existing IPC classification methods rely on rule-based or traditional ML models, which lack flexibility and scalability.

Dataset

• Contains 22,495 labeled case descriptions covering various crimes (theft, assault, fraud, etc.) mapped to 409 IPC sections.

• Cases are anonymized and preprocessed for training, with an 80-10-10 split for training, validation, and testing.

Problem Statement & Challenges

• Legal language complexity and ambiguous/incomplete case descriptions make classification difficult.

• Traditional methods require manual rules and struggle with nuances.

• The study aims to test lightweight transformers (DistilBERT, TinyBERT) for scalability and accuracy in real-world applications.

Methodology

• Dataset prepared with label encoding for IPC sections.

• TinyBERT chosen for efficiency and balanced performance; DistilBERT also evaluated.

• Case texts tokenized, padded, and encoded to feed into models.

• Models fine-tuned using cross-entropy loss, evaluated using accuracy and F1 score.

Results

• TinyBERT outperformed DistilBERT with 94% accuracy and 0.93 F1 score vs. 86% accuracy and 0.87 F1 for DistilBERT.

• TinyBERT’s smaller size and efficient design enable faster training and better real-time applicability.

• Both models face challenges handling complex legal jargon and class imbalance in the dataset.

Discussion

• Transformers are well-suited for legal text classification due to their context-awareness.

• Challenges remain, including ambiguous legal language and imbalanced data distribution.

• Continued improvements are needed for robust, fair automated legal classification.

Conclusion

This project successfully developed an intelligent system for predicting relevant IPC sections based on textual crime descriptions using a transformer-based machine learning model. By leveraging TinyBERT, the model was able to effectively process legal text, capturing linguistic patterns and contextual cues necessary for accurate classification. The training and fine-tuning process demonstrated promising results, with the model achieving high accuracy and a strong macro-averaged F1-score, ensuring balanced performance across both common and less frequent IPC sections. The system provides a significant advancement in legal automation, offering a tool that can assist law enforcement agencies, legal professionals, and judicial systems by streamlining the initial classification of cases. The ability to predict legal provisions based on natural language descriptions reduces manual effort, improves efficiency, and ensures consistency in legal documentation. Despite its strengths, certain challenges remain, particularly in handling underrepresented IPC sections where data availability is limited. Future work can focus on expanding the dataset, incorporating additional linguistic variations, and utilizing advanced NLP techniques to further enhance the model’s accuracy and robustness. Overall, this project demonstrates the potential of AI-driven legal support systems and paves the way for future innovations in legal text processing and predictive analytics.

References

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Copyright

Copyright © 2025 Amogh Kuppachi, Rohan R, Dr. Raghavendra V. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.