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This project builds a Convolutional Neural Network (CNN) model to recognize handwritten digits. The model is trained on a dataset of handwritten digits (like MNIST) and can accurately classify digits from 0 to 9.

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Name		Name	Last commit message	Last commit date
Latest commit History 9 Commits
app		app
notebook		notebook
README.md		README.md
requirements.txt		requirements.txt

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Handwritten Digit Recognition using CNN (TensorFlow)

This project uses a Convolutional Neural Network (CNN) to classify handwritten digits (0–9) from the MNIST dataset.

📌 Features

Uses TensorFlow/Keras
Achieves ~99% accuracy
Includes training history plots
Predicts on test samples

📂 Project Structure

notebook/mnist_cnn.ipynb → Jupyter notebook with full implementation
requirements.txt → Python dependencies

🚀 How to Run

pip install -r requirements.txt
jupyter notebook notebook/mnist_cnn.ipynb

About

This project builds a Convolutional Neural Network (CNN) model to recognize handwritten digits. The model is trained on a dataset of handwritten digits (like MNIST) and can accurately classify digits from 0 to 9.

keras cnn image-recognition tensor cnn-keras pytho dee mnsit-database

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