Handwritten Digit Recognition using CNN

This project is a Handwritten Digit Recognition system using Convolutional Neural Networks (CNNs), trained on the MNIST dataset. It includes a Streamlit web application that allows users to draw digits and get real-time predictions using a trained deep learning model.

Features ✅ CNN-based digit recognition trained on the MNIST dataset ✅ Interactive web app built using Streamlit ✅ Drawing canvas for users to input handwritten digits ✅ Real-time prediction with a pre-trained model ✅ Model saving & loading for deployment

Project Structure train_mnist.py: Contains the CNN model architecture and training script app.py: Streamlit application script mnist_cnn_model.h5: Pre-trained model saved in HDF5 format requirements.txt: List of dependencies Dataset The model is trained on the MNIST dataset, which consists of 60,000 training and 10,000 test grayscale images of handwritten digits (0-9), each of size 28x28 pixels.

Requirements

• Python 3.x
• TensorFlow
• Keras
• NumPy
• Matplotlib
• Scikit-learn

How to Run

1. Download saved model and app.py in Google Colab to same folder:

   • open cmd navigat ti that foler (cd %UserProfile%\Desktop\HR).

   • Run thi code in cmd (streamlit run app.py)

2. Alternatively, install dependencies and run locally:

   pip install -r requirements.txt
   jupyter notebook