Current Automatic Number Plate Recognition (ANPR) systems face challenges in accurately reading fancy or non-standard plates, leading to misidentification and inefficiencies. There is a need for a locally-developed ANPR module that can be customised to improve accuracy and better integrate with existing systems, particularly RFID toll infrastructure. This integration is crucial to validate vehicle identity, resolve disputes, and reduce fraudulent or erroneous transactions. The ideal solution would accommodate real-world conditions such as lighting, plate design variations, and high-speed capture environments.

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Desired Outcome

A robust ANPR module developed and supported by a local team, capable of accurately identifying standard and non-standard number plates in real time. The system should integrate seamlessly with existing RFID and toll systems to support vehicle verification, reduce misreads, and enhance transaction accuracy. It should include AI-enhanced OCR, image capture, and data validation features. Ideally, the module will be scalable and customisable for future use across different locations or systems.

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Potential Impact

Improved number plate recognition will lead to more secure, efficient toll operations and reduce user complaints related to double-charging or misidentification. This enhancement will help prevent toll fraud, support better traffic flow, and improve customer trust in automated tolling systems. A locally-developed, customisable ANPR solution also promotes domestic technology capabilities and reduces reliance on foreign vendors for critical infrastructure components.