Automated Crime Reporting and Charge Sheet Generation System Using Reinforcement

Abstract

Preparing crime records, First Information Reports (FIRs), and charge sheets by hand continues to be a serious obstacle in policing due to human mistakes, procedural slowdowns, and inconsistent record-keeping. To overcome these shortcomings, this study presents an AI-powered framework that handles FIR creation, crime categorization, IPC-based charge sheet drafting, and punishment estimation automatically. The framework combines machine learning, natural language processing, and legal data analytics to identify offense categories, link them to suitable IPC sections, and produce structured legal paperwork. Logistic Regression paired with text vectorization methods allows dependable crime classification, while predictive analytics assists in estimating sentences using past case records. This method boosts operational productivity, lowers dependence on manual effort, and guarantees consistency and transparency across law enforcement processes. Future developments may involve blockchain-based data protection and live integration with national crime databases.

Introduction

The text discusses how India’s criminal justice system still relies heavily on manual processes for FIR writing, evidence handling, and charge-sheet preparation, leading to delays, errors, and case backlogs. To address these issues, the proposed solution uses Artificial Intelligence (AI) and Natural Language Processing (NLP) to automate key legal tasks such as FIR generation, crime classification, IPC mapping, and sentencing prediction. Machine learning models like Logistic Regression, Naïve Bayes, and deep learning techniques help extract information from complaint texts and classify offenses efficiently, while also improving consistency and accuracy.

The literature review shows strong support for AI in legal informatics, highlighting its use in document analysis, crime classification, legal reasoning, and crime prediction. However, concerns about fairness, bias, and transparency are also emphasized.

The proposed system architecture includes modules for complaint processing, automated FIR generation, crime classification, evidence storage, charge-sheet creation, and sentencing prediction. NLP techniques extract structured information from unstructured complaints, while ML models classify crimes and suggest legal actions based on IPC sections and historical data.

Conclusion

This research establishes that combining NLP and ML can effectively automate the most critical parts of criminal justice workflows. Tasks such as FIR drafting, offense classification, IPC mapping, and sentencing prediction — which were previously handled manually and consumed significant time — can now be reliably managed by machines. The proposed system draws from well-established approaches including Logistic Regression, Seq2Seq reasoning models, crime prediction frameworks, and forensic AI tools [1]–[14], [16]. Future enhancements may include integration of transformer-based language models, blockchain-supported audit records, and real-time connectivity with national crime databases for complete end-to-end automation. These steps represent a major advancement toward a criminal justice system that is transparent, efficient, and driven by modern technology.

References

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Copyright

Copyright © 2026 Vikash Mallick, Tanmayi Jaiswal, Sujal Dhurve, Arpita Bankar, Sushama Telrandhe, Rakesh Moharle. 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.