This project implements a simple handwritten digit recognition system using TensorFlow and Matplotlib. The model is trained on the MNIST dataset, which contains 70,000 grayscale images of handwritten digits (0-9).
- Draw digits on a 28x28 pixel grid.
- Predict the drawn digit using a trained neural network model.
- Clear the canvas to start a new drawing.
To run this project, you need to have Python installed on your machine. You can then install the required packages by following these steps:
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Clone the repository: git clone https://github.com/Savdekaryashu/Digit-Recognition.git cd Digit-Recognition
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Installing requirements: pip install -r requirements.txt
Run the Predictor.py script A window will open where you can draw a digit using your mouse. Press the "Predict" button to see the model's prediction. Press the "Clear" button to reset the drawing area.
The model is trained on the MNIST dataset using a simple neural network architecture. When you draw a digit, it gets transformed into a 28x28 pixel grayscale image. The model then predicts the digit based on the drawn image.
This project uses the MNIST dataset, which is a benchmark dataset for handwritten digit recognition. Thanks to TensorFlow and Matplotlib for providing powerful libraries for deep learning and data visualization.