The limitation of existing ALPR systems, is that they fail to adapt to number plates varying due to region or location. Custom numbers plates are another example where existing systems fail to deliver results.

The solution consists of a computer vision based approach that would detect, read and identify number plates of moving vehicles using CCTV cameras and then store them into a centralized database. This system can be deployed in restricted and high security areas or gated communities to monitor the movement of vehicles entering, exiting or inside the premises.

ByteCorp developed an inhouse ALPR system using custom deep learning models trained on a large diverse dataset, coupled with a unique approach for a single solution that caters to all types of Number Plates including custom number plates.

LPR consists of a wide range of features, being a technology of the modern AI era, it offers the following:

• Integration to your existing application or can be developed as a different use case.
• Uses deep learning techniques to improve matching and detection algorithms.
• inputs can be a video or still.
• Fully managed cloud-based solution, or can be deployed in-house i.e. locally.
• Choice of Technologies dependent upon use case and security requirements.
• Fast, Robust, and Precise Detection and Verification.
• No External hardware is required. Provides a complete and easy-to-use software interface.

LPR is a technology that offers several benefits that includes:

• The solution would detect, read, and identify number plates.
• The number plates can be of varying sizes, locations, and fonts.
• The data will be saved into a system.
• The system can be deployed on the edge for ease of scalability and cost reduction.

ByteCorp worked on developing the technology and the solution by first identifying the limitations of existing and competitor system. Interviews were conducted with potentials customers to pin point he exact requirements, and then a R&D cycle was initiated to develop the solution.