Crime Story Analysis Using NLP and ML with Django along with Voice

Abstract

This project develops an advanced crime prediction and analysis system that utilizes natural language processing (NLP) and machine learning techniques to process crime narratives and offer actionable insights. The system is designed to analyse crime stories by extracting crucial information such as potential suspects, motives, opportunities, and the likelihood of specific crime categories. It employs various machine learning models, including deep learning approaches, to understand and classify the narratives efficiently. The system incorporates speech recognition, enabling users to input crime stories through voice commands, while the text-to-speech functionality allows for an interactive and seamless user experience. This combination of technologies makes the system more intuitive for law enforcement personnel and investigators, allowing them to quickly gather and understand relevant data without needing extensive training on technical aspects. At its core, the system aims to assist law enforcement agencies in predicting crime categories, identifying patterns in criminal activities, and profiling suspects based on narrative-driven information. By analysing the context and relationships within the stories, the system helps investigators focus on high-priority suspects and critical elements in each case. The backend is developed using Django, providing a robust platform for web-based crime analysis, while the integration of NLP libraries such as spaCy and Transformers enables the system to perform advanced text processing and model inference. The project's implementation provides a cutting-edge tool for improving the speed, accuracy, and efficiency of crime analysis in law enforcement agencies.