Building a License Plate Recognition Engine in C++ — Part 1: Image Loading and Core LPR Data Structures

In this series, I’ll build a License Plate Recognition (LPR) engine step by step in C++. The goal is not only to recognize license plates but also to understand the internal mechanics behind image processing and recognition systems.

In this first part we will:

• Load an image using OpenCV
• Convert the image into grayscale
• Introduce the core data structures used by the recognition engine

Future parts will cover:

• Image preprocessing
• Noise reduction
• Edge detection
• Plate localization
• Character segmentation
• Character recognition
• Performance optimization

Loading an image

The first step of any vision system is acquiring image data.

We read the image path from command-line arguments:

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

int main(int argc, char* argv[])
{
    if (argc < 2)
    {
        std::cout << "Usage: app.exe <image_path>\n";
        return -1;
    }

    const char* imagePath = argv[1];

    cv::Mat image = cv::imread(imagePath);

    if (image.empty())
    {
        std::cout << "Failed to load image: " << imagePath << "\n";
        return -1;
    }

    cv::Mat gray;
    cv::cvtColor(image, gray, cv::COLOR_BGR2GRAY);

    LPRResult carData;

    CRecogEngine recogEngine;

    int nPlate = recogEngine.Recognition(
        gray.data,
        gray.cols,
        gray.rows,
        &carData);

    std::cout << "Recognition completed.\n";

    return 0;
}

Why convert to grayscale?

Most early image-processing stages do not require color information.

Using grayscale provides several advantages:

• Reduces memory usage
• Simplifies calculations
• Improves processing speed
• Keeps intensity information needed for edge and pattern analysis

Instead of processing three channels (BGR), we process a single intensity channel.

Designing core LPR data structures

Recognition systems need a structured way to store results.

The following structures define the fundamental objects used by our engine:

#pragma once

#define MULTIRESULT 10

typedef struct LPRRect
{
    long    left;
    long    top;
    long    right;
    long    bottom;
} Rect;


typedef struct LPRPoint
{
    int x;
    int y;
} Point;

typedef struct LPRSize
{
    int width;
    int height;
} Size;

typedef struct PlateCandidate
{
    int     characterCount;
    char    licenseText[20];
    float   confidenceDistance;
    int     confidenceScore;
    Rect    plateRegion;            
    Rect    characterRegions[20];
} LICENSE, * LPLICENSE;

struct LPRResult
{
    int     nPlate;
    LICENSE pPlate[MULTIRESULT];
    int     nProcTime;
};

These structures serve different purposes:

LPRRect

• Stores coordinate information

PlateCandidate

• Stores recognized text
• Stores confidence values
• Stores plate position
• Stores character locations

LPRResult

• Stores all recognition outputs
• Supports multiple detected plates

Data flow of our engine

The recognition pipeline currently looks like this:

Input Image
      ↓
Image Loading
      ↓
Grayscale Conversion
      ↓
Recognition Engine
      ↓
LPRResult

At this point, the system can load an image and pass grayscale pixel data into the recognition engine.

In the next article, we'll begin preprocessing the image to prepare it for plate detection.