International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 12 | Dec 2025
p-ISSN: 2395-0072
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Automated Crime Reporting and Charge sheet Generation System Using Reinforcement Vikash Mallick1, Tanmayi Jaiswal2, Sujal Dhurve3, Arpita Bankar4, Rakesh Moharle5 , Sushama Telrandhe6 1,2,3,4Dept of CSE, Gurunanak Institute of Engineering and Technology, Nagpur, Maharashtra, India 5Assistant Professor, Dept of CSE, Gurunanak Institute of Engineering and Technology, Nagpur, Maharashtra, India 6Associate Professor, Dept of CSE, Gurunanak Institue of Engineering and Technology, Nagpur, Maharashtra, India
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Abstract - Manual preparation of crime records, First
complaints, enabling automated processing of narrative descriptions [1], [12]. ML classification algorithms, especially Logistic Regression, Naïve Bayes, and XG Boost, have been widely used to classify legal documents and crime types with high accuracy [2], [5]. Similarly, predictive systems have shown promising results in identifying crime trends, forecasting potential criminal behaviour, and estimating judicial outcomes [9].
Information Reports (FIRs), and charge-sheets remains a significant bottleneck in policing due to human error, procedural delays, and inconsistencies in documentation. To address these limitations, this research introduces an AIdriven framework that automates FIR drafting, crime categorization, IPC-based charge-sheet preparation, and penalty estimation. The system integrates machine learning, natural language processing, and legal-data analytics to classify offense types, map them to appropriate IPC sections, and generate structured legal documents. Logistic Regression combined with text-vectorization techniques enables reliable crime classification, while predictive analytics supports sentencing estimation based on historical case data. The approach enhances operational efficiency, reduces manual dependency, and ensures uniformity and transparency in law-enforcement workflows. Future extensions may include block chain-based data security and real-time integration with national crime databases.
By integrating these capabilities, the proposed system enables automatic FIR drafting, offense categorization, IPC-mapping, and sentencing prediction. Such automation aligns with global trends where legal AI systems are increasingly used for document screening, case-law retrieval, fraud detection, and judicial decision support [12], [15]. The system thus enhances efficiency, transparency, and fairness within law-enforcement procedures.
2. LITERATURE SURVEY
Key Words: Automated Crime Reporting, FIR Generation, Charge-Sheet Automation, Crime Classification, Logistic Regression, Natural Language Processing, Indian Penal Code (IPC), Judicial Automation, Sentencing Prediction, Predictive Analytics, Machine Learning in Law Enforcement, Legal Document Processing, Crime Data Analysis, AIAssisted Policing, Digital Forensics Integration.
AI and NLP have become integral tools in modern legal informatics due to their ability to interpret, categorize, and analyse complex textual information. Several major research works highlight their application in criminal justice: [1] NLP for Legal Documentation Studies such as Vattikuti (2024) demonstrate that transformer-based models like BERT and GPT significantly improve clause extraction, anomaly detection, and document review accuracy in legal workflows [1]. Such systems reduce reliance on manual reading and improve consistency.
1. INTRODUCTION Despite major digital governance initiatives, core components of India’s criminal justice system still rely heavily on manual workflows such as FIR drafting, chargesheet preparation, evidence logging, and IPC-based classification. Such manual dependency increases the likelihood of human error, procedural delays, and inconsistent interpretation of legal guidelines [4]. As police departments process thousands of incident reports daily, these inefficiencies contribute to overburdened courts, case backlogs, and delayed justice delivery.
[2] Automated Crime Classification Ku & Leroy (2013) developed a decision-support system using NLP and Naïve Bayes classifiers to categorize crime reports. Their system achieved an accuracy of 94.82%, surpassing human analysts [2]. This demonstrates the reliability of ML in automated crime categorization. [3] Charge Prediction and Legal Reasoning Ye et al. (2018) introduced a text-to-text model generating judicial reasoning (“court views”) from case descriptions.
AI-driven automation offers transformative potential to address these gaps. NLP methods provide accurate extraction of semantic information from textual
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