MATLAB Implementation for Automatic License Plate Recognition Using Hough Transform

This article discusses automatic license plate recognition technology, which finds applications in various practical scenarios. Specifically, it can be implemented in intelligent transportation systems, parking lot management systems, and numerous other situations requiring vehicle identification and tracking. The system utilizes Hough Transform methodology to correct license plate tilt, ensuring image accuracy and reliability for subsequent processing stages. The Hough Transform represents a powerful mathematical tool with broad applications across multiple domains, including computer vision, image processing, and machine learning. In MATLAB implementation, the Hough Transform algorithm typically involves these key steps: edge detection using Canny or Sobel operators, Hough line detection to identify dominant angles, and affine transformation for image rotation correction. The core function `hough()` in MATLAB's Image Processing Toolbox calculates the Hough transform matrix, while `houghpeaks()` identifies peak values corresponding to straight lines in the original image. For license plate recognition systems, the tilt correction process significantly improves character segmentation accuracy by normalizing the license plate orientation before OCR processing. The implementation typically includes preprocessing stages like image binarization, morphological operations for noise removal, and contour detection to isolate the license plate region before applying Hough-based geometric corrections.