License Plate Recognition Using MATLAB: Implementation and Code-Based Approaches

Implementing a license plate recognition system in MATLAB represents a classic digital image processing application scenario. The workflow can be divided into three main stages: license plate localization, character segmentation, and character recognition.

License Plate Localization Stage The process begins with image preprocessing techniques such as grayscale conversion and edge detection to enhance license plate region features. MATLAB's rgb2gray() function converts color images to grayscale, while edge() with Canny or Sobel operators detects boundaries. Color space analysis (e.g., saturation component in HSV space using rgb2hsv()) or morphological processing (closing operations with imclose() to connect broken edges) helps filter candidate regions. Common localization algorithms include texture-based methods leveraging the high-frequency characteristics of license plate characters or template matching approaches. Final precise positioning is achieved through geometric constraints like aspect ratio validation using regionprops() function to analyze bounding box properties.

Character Segmentation Stage After successful localization, the license plate undergoes binarization using imbinarize() or adaptive thresholding, followed by skew correction through Hough transform or rotation angle calculation. Vertical projection analysis determines segmentation boundaries by identifying blank spaces between characters. For handling connected characters (such as combined "京" and "A"), techniques like connected component analysis with bwconncomp() or adaptive threshold segmentation may be employed. MATLAB's imrotate() function helps correct tilting, while differential projection methods identify character gaps.

Character Recognition Stage Traditional approaches extract character features using HOG (via extractHOGFeatures()) or LBP (through extractLBPFeatures()) descriptors, combined with SVM classifiers (fitcsvm()) or neural networks (patternnet()). For deep learning implementations, architectures like AlexNet can enable end-to-end recognition using transfer learning with alexnet(), though training datasets must comprehensively cover variants under different lighting conditions and plate damages. MATLAB's Deep Learning Toolbox provides pretrained networks that can be fine-tuned for specific license plate recognition tasks.

Optimization directions include incorporating super-resolution techniques to enhance low-resolution plate clarity using imresize() with interpolation methods, or multi-frame fusion to improve recognition rates in dynamic video sequences via VideoReader() and frame averaging. Practical deployment must account for formatting differences across countries/regions, which can be addressed through region-specific template databases or machine learning models trained on diverse datasets.