MATLAB-based License Plate Recognition System for Night Conditions

In this research project, we developed a license plate recognition methodology using binary image processing and histogram analysis specifically optimized for nighttime conditions. The implementation involves MATLAB-based image processing techniques where we first convert the input image to a binary format using adaptive thresholding methods like Otsu's algorithm or local thresholding to handle varying lighting conditions. Following binarization, we calculate luminance values for each pixel and generate comprehensive histograms to analyze brightness distribution patterns.

Through systematic analysis of histogram characteristics such as peak distributions, valley points, and statistical properties, the algorithm identifies candidate license plate regions in low-light environments. Key MATLAB functions employed include im2bw() or imbinarize() for image binarization, imhist() for histogram generation, and custom statistical functions for feature extraction. This approach enables precise detection of nighttime operational sequences and facilitates subsequent analytical refinements to enhance recognition accuracy and system reliability under challenging lighting conditions.