Automatic License Plate Detection and Recognition for Vehicle Surveillance Applications
Abstract
Automatic recognition of vehicle number plates plays a critical role in modern traffic surveillance and law enforcement. The proposed system uses Convolutional Neural Networks (CNNs), a machine learning algorithm, to accurately detect and isolate number plates from full vehicle images. To enhance the clarity of plate boundaries, edge detection techniques are applied before character segmentation. The segmented characters are then processed using Optical Character Recognition (OCR) to extract the alphanumeric information. Using the recognized vehicle number, registration details can be retrieved from a connected server. In cases of traffic violations or suspicious activity, authorities can take appropriate actions based on this information. This system is highly applicable in traffic monitoring, public safety, and law enforcement, contributing to more efficient and automated transportation management.
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