🔢 Project : Handwritten Digit Classifier (CNN-based)

A real-time digit recognition system using Convolutional Neural Networks trained on the MNIST dataset.

🚀 Overview

This project uses deep learning to classify handwritten digits (0–9) from images. It is trained on the MNIST dataset and deployed via Streamlit with an interactive UI that lets users draw a digit or upload an image for prediction.

🧠 How It Works

1. 🖼 Image Input
   Users can draw a digit on the canvas or upload a 28x28 pixel grayscale image.

2. 🔄 Preprocessing
   The image is reshaped and normalized before being fed to the model.

3. 🧠 Model Architecture
   A CNN with multiple layers:

   • Conv2D + ReLU + MaxPooling
   • Dense (Fully Connected)
   • Softmax output layer for 10-class classification

4. ⚡ Prediction
   The model predicts the digit with high accuracy, and displays the result instantly.

💻 Tech Stack

| Tool            | Description                              |
|-----------------|------------------------------------------|
| Python        | Core programming language                |
| TensorFlow/Keras | For building and training the CNN    |
| Streamlit     | User-friendly web app interface          |
| NumPy | For image handling and preprocessing     |
| MNIST         | Benchmark dataset for handwritten digits |```

### 🔍 Example Predictions
```bash
| Input Image        | Predicted Digit |
|--------------------|-----------------|
| (Drawn "5")        | 5 ✅             |
| (Uploaded "3.png") | 3 ✅             |