License Plate Recognition

AI license plate recognition systems, when handling tilted license plates, backlighting, and other scenarios in complex environments, are more advantageous than traditional license plate recognition systems, with lower long-term maintenance costs and greater functional scalability.

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Project Description

Traditional license plate recognition systems (rule-based LPR), which rely heavily on manually designed features and fixed rules, perform poorly in processing data from dynamic traffic environments, and cannot adapt on their own to unseen samples. NU-LPR can recognize various license plates and is effective in complex environments.

Project Challenges

Since AI’s strength comes from data-driven inference, it is necessary to collect environmental data during model training (dawn, dusk, rainy days).

Solution

By using data collected across various environments, together with data augmentation techniques, the license plate recognition module can identify various license plates in different environments.

cars parked on parking lot during daytime

Result

Works with third-party systems to provide functions such as whitelists/blacklists, parking duration, and reserved parking.

Successfully applying this system in complex environments improves license plate recognition rates, and the software is highly flexible and can be customized according to customer needs.