---
title: DigitRecognitionCNN
emoji: ✍️
colorFrom: red
colorTo: red
sdk: streamlit
app_file: src/streamlit_app.py
pinned: false
short_description: Handwritten digit recognition (0–9) using a CNN and MNIST.
license: mit
---

# ✍️ MNIST Digit Recognizer (CNN)

This project is a simple Computer Vision application that recognizes handwritten digits (0–9) using a Convolutional Neural Network (CNN).

## Dataset
- Kaggle Competition: **Digit Recognizer (MNIST)**
- Each image is **28×28 grayscale** (784 pixels).
- Labels are digits **0–9**.

## Approach
1. Load `train.csv` and `test.csv`
2. Normalize pixel values to **[0, 1]**
3. Reshape to **(28, 28, 1)**
4. Train a CNN with early stopping
5. Evaluate using validation accuracy and confusion matrix
6. Generate `submission.csv` for Kaggle

## Results
- The model achieved around **~99% validation accuracy**.
- Most digits are predicted correctly; errors mostly happen for visually similar digits.

## Streamlit App
The app lets you upload an image of a handwritten digit and outputs:
- predicted digit
- confidence score
- probability distribution for all 10 digits
- preprocessed 28×28 image

## Files
Recommended structure:

.
├─ app.py
├─ best_mnist_cnn.keras
├─ requirements.txt
└─ README.md

perl
Code kopieren

## How to run locally
```bash
pip install -r requirements.txt
streamlit run app.py
Notes
For best predictions, upload an image with one digit, centered, with a clean background.