NeuralDigit - NeuralDigit Neural Network Digit Recognition (C++)

This C++ project implements a neural network for handwritten digit recognition using the MNIST dataset. The network architecture consists of three layers with 785, 30, and 10 neurons respectively.

Online Demo

An online demonstration is available where you can try out the trained model. Visit NeuralDigit Demo to test the model with your own handwritten digits and see how well it performs.

Image Preprocessing (Web Version)

The process begins by scaling down the bounding box of the input drawing to a size of 20x20 pixels. The image is then centered on the center of mass of the pixels within a 28x28 image. This preprocessing step prepares the image for feeding it into the pre-trained neural network.

Pre-Trained Model (Web Version)

The neural network model used in this project is pre-trained using the MNIST dataset, following the same instructions as the MNIST training data. The model has learned to recognize handwritten digits based on this training.

Usage

Running the Neural Network

1. Clone this repository and navigate to the project directory.
2. Extract the Data.zip file located in the data directory.
3. Compile the project:

   g++  NeuralDigit.cpp -o NeuralDigit

4. Run the neural network:

   ./NeuralDigit

Input Data Format

The necessary input files for training and testing are located inside the data directory:

• train-images.idx3-ubyte
• train-labels.idx1-ubyte
• t10k-images.idx3-ubyte
• t10k-labels.idx1-ubyte

These files contain the MNIST dataset images and labels.

Feel free to contribute, report issues, or make suggestions for improvements!