Handwriting Recognition Gaming Project in C++

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3 months ago

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

using namespace std;
using namespace cv;
using namespace cv::ml;

int main() {
    // Load the pre-trained KNN model
    Ptr<KNearest> knn = KNearest::create();
    knn->read("knn_model.xml");  // The model file should be pre-trained and saved

    // Load the handwritten digit image
    Mat img = imread("handwritten_digit.png", IMREAD_GRAYSCALE);
    if (img.empty()) {
        cerr << "Error loading image!" << endl;
        return -1;
    }

    // Preprocess the image
    Mat resized_img, flattened_img;
    resize(img, resized_img, Size(28, 28));  // Resize image to match training size
    resized_img.convertTo(resized_img, CV_32F);  // Convert to float for KNN
    flattened_img = resized_img.reshape(1, 1);  // Flatten to 1D vector

    // Predict the digit
    Mat result;
    knn->findNearest(flattened_img, 1, result);

    // Output the result
    cout << "Predicted digit: " << result.at<float>(0, 0) << endl;

    return 0;
}

#include <iostream>

#include <opencv2/opencv.hpp>

#include <opencv2/ml.hpp>

using namespace std;

using namespace cv;

using namespace cv::ml;

int main() {

// Load the pre-trained KNN model

Ptr<KNearest> knn = KNearest::create();

knn->read("knn_model.xml"); // The model file should be pre-trained and saved

// Load the handwritten digit image

Mat img = imread("handwritten_digit.png", IMREAD_GRAYSCALE);

if (img.empty()) {

cerr << "Error loading image!" << endl;

return -1;

}

// Preprocess the image

Mat resized_img, flattened_img;

resize(img, resized_img, Size(28, 28)); // Resize image to match training size

resized_img.convertTo(resized_img, CV_32F); // Convert to float for KNN

flattened_img = resized_img.reshape(1, 1); // Flatten to 1D vector

// Predict the digit

Mat result;

knn->findNearest(flattened_img, 1, result);

// Output the result

cout << "Predicted digit: " << result.at<float>(0, 0) << endl;

return 0;

}

Explanation:

Dependencies:
- OpenCV is used for image processing and machine learning functions. You need to have OpenCV installed and properly linked with your project.
Load Pre-trained Model:
- Ptr<KNearest> knn = KNearest::create(); creates a KNN object.
- knn->read("knn_model.xml"); loads a pre-trained KNN model from an XML file. You need to have this model pre-trained and saved.
Load and Preprocess Image:
- imread("handwritten_digit.png", IMREAD_GRAYSCALE); loads the handwritten digit image in grayscale mode.
- resize(img, resized_img, Size(28, 28)); resizes the image to 28×28 pixels to match the training size.
- resized_img.convertTo(resized_img, CV_32F); converts the image to a floating-point format suitable for KNN.
- flattened_img = resized_img.reshape(1, 1); flattens the 2D image to a 1D vector.
Predict the Digit:
- knn->findNearest(flattened_img, 1, result); performs the KNN classification and stores the result.
- result.at<float>(0, 0) contains the predicted digit.
Output the Result:
- cout << "Predicted digit: " << result.at<float>(0, 0) << endl; prints the predicted digit.

Requirements:

Pre-trained Model: You need a pre-trained KNN model to recognize handwritten digits. You can train this model using a dataset like MNIST and save it as knn_model.xml.
Handwritten Digit Image: Ensure you have an image file named handwritten_digit.png

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with a digit to test.

Possible Enhancements:

Improved Accuracy: Use more sophisticated algorithms or deep learning models for better accuracy.
Real-time Recognition: Implement real-time recognition using a camera and dynamic preprocessing.
GUI Integration: Create a graphical user interface to allow users to draw digits and see predictions interactively.

Explanation:

Requirements:

Possible Enhancements:

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