MATLAB-Based License Plate Recognition [Detailed Comments]

(1) Image Preprocessing: Perform image conversion, image enhancement, and edge detection on automotive images. This step optimizes automotive images for subsequent processing and analysis. Specific operations include color space conversion (e.g., RGB to grayscale using rgb2gray()), contrast enhancement (via histeq() or adapthisteq()), and edge detection (using Canny or Sobel operators with edge() function) to improve image quality and clarity. These operations are typically implemented through MATLAB's Image Processing Toolbox functions.

(2) License Plate Localization: Segment the license plate image from preprocessed automotive images, i.e., locate the license plate position within a vehicle image. This step extracts the license plate region for subsequent character segmentation and recognition. Implementation commonly involves region-based segmentation using morphological operations (imopen(), imclose()) or color/shape-based detection algorithms. The regionprops() function can be used to identify rectangular regions meeting specific aspect ratio and size criteria typical of license plates.

(3) Character Segmentation: Perform geometric correction, noise removal, binarization, and character segmentation on the license plate image to isolate individual character images composing the license plate number. This step begins with geometric correction using imrotate() or affine transformations for vertical alignment. Noise removal employs filters like medfilt2() or wiener2(). Binarization uses adaptive thresholding (imbinarize() with adaptive method) to separate characters from background. Character segmentation utilizes vertical projection analysis or connected component analysis (bwconncomp()) to split characters based on inter-character spacing, yielding individual character images.

(4) Character Recognition: Preprocess segmented characters (binarization, normalization), then analyze and extract features to recognize segmented character images and output the license plate number in text format. This step employs machine learning algorithms (like SVM using fitcsvm()) or pattern recognition techniques (template matching via normxcorr2()). Preprocessing involves binarization (imbinarize()) and normalization (imresize() for standard dimensions) to better extract features using regionprops() or HOG features, improving recognition accuracy and reliability through classifiers like CNN (using Deep Learning Toolbox) or OCR functions (ocr()).