Neural Network and Color-Based License Plate Recognition with Plate Segmentation

In this article, we will explore license plate recognition technology based on neural networks and color processing combined with plate segmentation. This technology can be applied in various scenarios such as smart parking systems, road surveillance, and traffic monitoring. Specifically, we will discuss the operational workflow of this approach, its advantages, and practical application domains. The implementation typically involves multiple processing stages: color space conversion for plate region detection, morphological operations for noise reduction, contour analysis for plate localization, and convolutional neural networks (CNN) for character segmentation and recognition. Key algorithms include HSV color thresholding for plate candidate extraction, connected component analysis for plate validation, and deep learning models for optical character recognition (OCR). Detailed software specifications and implementation guidelines are provided in the appendix of this article.