International Research Journal of Education and Technology

License License plate recognition plays a key role in a variety of applications, including automated vehicle identification, safety monitoring, and traffic management. Due to the growing need for automated and efficient vehicle detection systems, many methods have been developed for identifying license plates from vehicle images and video flows. This paper provides a detailed review of various license key detection techniques using OpenCV, an extensive computer vision library. First, this paper describes fundamental challenges in license plate detection, including plate size, shape, lighting conditions, and variations in occlusion. This paper classifies common methods into traditional image processing techniques with deep learning and modern approaches.

Traditional methods often use a combination of techniques, such as preprocessing , edge detection,  regional segmentation, and morphological  manipulation. In contrast, deep learningbased approaches with foldable folding networks and regional folding networks use more robust detection under difficult conditions. Integrating OPENCV into these methods enables real-time processing and efficient processing of large image data records. This paper also illustrates a hybrid approach combining traditional computer vision techniques with deep learning models to improve accuracy and speed. We discuss key performance metrics such as recognition rate, localization accuracy, and computational efficiency, and discuss comparative analysis of the advantages and disadvantages of each method. Finally, this paper discusses potential future directions in license plate detection and highlights the role of OPENCV in enabling practical and usable solutions in real-world scenarios such as Smart City infrastructure, law enforcement, and autonomous driving systems.