Digit-Recognition-CNN-

This project leverages Convolutional Neural Networks (CNNs) to accurately classify handwritten digits using the MNIST dataset. Implemented with TensorFlow/Keras, the model is designed to effectively recognize and predict digits from grayscale images, showcasing advanced image classification capabilities.

📁 About Dataset

• Training and Testing Data:
  • train.csv: Contains training data with pixel values and corresponding labels.
  • test.csv: Contains test data with pixel values for prediction.
  • sample_submission.csv: Provides a sample format for submission.

🛠️ Getting Started

Follow these steps to set up and run the project on your local machine.

Prerequisites

Make sure you have Python 3.7+ and Jupyter installed. You can check your Python version with:

python --version

To install Jupyter, run:

pip install notebook

Installation

Install the required dependencies:

1. Clone the repository:

   git clone https://github.com/ganayasser/Digit-Recognizer-CNN.git
   cd Digit-Recognizer-CNN

2. Install the required dependencies using the requirements.txt file:

   pip install -r requirements.txt

📝Running the Notebook

1. Start Jupyter Notebook:

jupyter notebook

2. Open the notebook file DigitRecognizer.ipynb from the Jupyter interface.
3. Run the cells to generate images based on text prompts.

📧Contact

For any questions or inquiries, feel free to reach out to me:

• Email: [email protected]