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Punjab launches AI war on crime with IIT Ropar

The initiative will help compile structured and unstructured data for a comprehensive database of criminals. This includes scanned PDFs and handwritten crime record, which will be digitised and unified into a single system.

Published on: Apr 17, 2026 09:16 AM IST
By , Chandigarh
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Intensifying its fight against organised crime and drugs forward, the Punjab government has partnered with the Indian Institute of Technology (IIT) Ropar to develop an artificial intelligence (AI)-powered crime prevention system.

Under this tie-up, IIT Ropar will employ AI tools to develop an advanced software, equipped with data analysis, voice recognition and dashboard-based monitoring, for real-time tracking of criminal networks and intelligence-led policing.
Under this tie-up, IIT Ropar will employ AI tools to develop an advanced software, equipped with data analysis, voice recognition and dashboard-based monitoring, for real-time tracking of criminal networks and intelligence-led policing.

Under this tie-up, IIT Ropar will employ AI tools to develop an advanced software, equipped with data analysis, voice recognition and dashboard-based monitoring, for real-time tracking of criminal networks and intelligence-led policing.

The initiative will help compile structured and unstructured data for a comprehensive database of criminals. This includes scanned PDFs and handwritten crime record, which will be digitised and unified into a single system.

This crime data will be converted into actionable intelligence, predictive models and analytical dashboards — enabling faster processing and meaningful insights.

“Data plays a crucial role in modern policing. While structured data in organised formats is easy to analyse, a large portion of police records exists in unstructured forms like scanned documents and handwritten reports. Integrating these remains a challenge, often slowing investigations,” said the project head, while adding that this project will bridge that gap by converting unstructured records and combining them with existing datasets into a unified system.

 
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