STM32 microcontrollers are widely used in embedded system development due to their robust performance and extensive peripheral support. This article explores the implementation of a license plate recognition system using an STM32 platform, focusing on the core image processing stages.

License plate recognition involves extracting alphanumeric characters from vehicle plates through digital image analysis. The typical workflow consists of four primary phases:

1. Image Acquisition: Capturing the license plate image using a camera module.
2. Image Preprocessing: Enhancing the raw image through grayscale conversion, binarization, and noise reduction.
3. Character Segmentation: Isolating individual characters from the processed plate region.
4. Character Recognition: Identifying each segmented character using feature extraction and classification algorithms.

This guide details the implementation of the preprocessing and recognition stages with practical code examples.

Image Preprocessing Example

Preprocessing prepares the image for segmentation by increasing contrast and removing irrelevant details.

#include "opencv2/opencv.hpp"

int main() {
    // Load the license plate image in grayscale
    cv::Mat inputImage = cv::imread("license_plate.jpg", cv::IMREAD_GRAYSCALE);
    if (inputImage.empty()) {
        std::cerr << "Error: Could not load image.\n";
        return -1;
    }

    // Apply binary thresholding
    cv::Mat binaryOutput;
    cv::threshold(inputImage, binaryOutput, 128, 255, cv::THRESH_BINARY_INV);

    // Display the result
    cv::imshow("Processed Plate Image", binaryOutput);
    cv::waitKey(0);

    return 0;
}

This code loads a grayscale image and applies inverse binary thresholding. Pixels above a value of 128 are set too white (255), and those below are set to black (0), creating a high-contrast image ideal for character segmentation.

Character Recognition Example

After segmentation, individual character images are classified. This example outlines the structure for feature extraction and classification.

#include <iostream>
#include "opencv2/opencv.hpp"

// Function prototypes
cv::Mat computeCharacterFeatures(const cv::Mat& charImage);
char predictCharacterClass(const cv::Mat& featureVector);

int main() {
    // Load a segmented character image
    cv::Mat charImg = cv::imread("char_A.png", cv::IMREAD_GRAYSCALE);
    if (charImg.empty()) {
        std::cerr << "Error: Character image not found.\n";
        return -1;
    }

    // Extract numerical features
    cv::Mat featureSet = computeCharacterFeatures(charImg);

    // Classify the character
    char result = predictCharacterClass(featureSet);

    std::cout << "Predicted Character: " << result << std::endl;
    return 0;
}

cv::Mat computeCharacterFeatures(const cv::Mat& charImage) {
    cv::Mat features;
    // Example: Calculate Histogram of Oriented Gradients (HOG) features
    // Implementation specific to chosen algorithm
    return features;
}

char predictCharacterClass(const cv::Mat& featureVector) {
    char predictedChar;
    // Example: Use a trained model (e.g., SVM, Neural Network) for prediction
    // Implementation depends on the selected classifier
    return predictedChar;
}

This framework demonstrates the recognition pipeline. The computeCharacterFeatures function generates a numerical descriptor (like HOG or zoning features) from the character image. The predictCharacterClass function then uses this descriptor with a pre-trained model to output the final character.

Implementing a full license plate recognition system on STM32 involves integrating these algorithms with hardware constraints, potentially using optimized libraries or lighter-weight alternatives to OpenCV for resource-limited environments.