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The client is a U.S.-based technology provider supporting law enforcement with surveillance and license plate recognition solutions. Its system enables patrol units, detectives, and command staff to identify and prevent crime, allowing officers to scan over 30,000 plates monthly using mobile readers for efficient, contactless compliance.
Country
USA
Industry
Artificial Intelligence
Services Used
The client was working with existing ALPR systems that involved high operational costs and had limitations in handling high-speed vehicles. There were also considerations around data consistency and hardware compatibility, which influenced performance across certain law enforcement scenarios.
The client aimed to build an ML-driven ALPR application to improve speed and accuracy. While exploring experienced AI partners, they discovered Daffodil Software’s AI Center of Excellence (CoE). They required real-time plate detection, instant vehicle insights, continuous model improvement, strong on-device processing, and the ability to handle image inconsistencies while adapting to evolving hardware and data needs.
Daffodil was selected as the technology partner and created a clear development roadmap based on the client’s requirements:
Develop trained models for instant license plate scanning within milliseconds
Retrieve the state of origin, vehicle make, and model instantly
Train and retrain models for progressively faster predictions
Enable most data processing on the client-side
Calibrate for image issues like noise and improper orientation
Update models regularly to maintain compatibility with hardware upgrades
The Daffodil team developed an ML-based web and mobile application designed to scan license plates of moving vehicles and extract relevant vehicle and ownership details. The solution was built to support law enforcement personnel with timely and accurate insights using existing mobile devices and surveillance systems.
The application focused on enabling real-time detection, faster data access, and improved decision-making in the field. By combining custom ML models with efficient processing, the system was designed to deliver consistent performance across varied conditions while supporting ongoing model improvements and operational efficiency.
The ML-powered mobile and web application was built with the following key capabilities:
With Daffodil’s AI expertise, the client enabled law enforcement agencies to use their existing infrastructure for more proactive policing. The ML-based ALPR system allowed officers to act on historical leads from the National Crime Information Center (NCIC) and streamline investigations. The solution has scanned over 80,000 vehicles with an accuracy rate of 98.5%, improving reliability in real-world conditions.
The system also enabled faster follow-ups on long-pending cases by improving access to relevant data and insights. With 10x faster scanning and reporting, law enforcement teams could respond more efficiently in the field. The client acknowledged Daffodil’s ability to deliver results that aligned well with their operational expectations.
80,000+
Vehicles Scanned
98.5%
Accuracy
10x
Faster Scanning & Reporting
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