https://github.com/pelayo-felgueroso/mnist-paint-classifier
Train a CNN on MNIST and classify digits drawn in Paint
https://github.com/pelayo-felgueroso/mnist-paint-classifier
cnn-classification cnn-for-visual-recognition cnn-keras cnn-model computer-vision digit-recognition digit-recognition-mnist educational educational-project image-classification mnist mnist-classification python tensorflow tutorial
Last synced: 2 months ago
JSON representation
Train a CNN on MNIST and classify digits drawn in Paint
- Host: GitHub
- URL: https://github.com/pelayo-felgueroso/mnist-paint-classifier
- Owner: pelayo-felgueroso
- Created: 2025-04-06T18:34:08.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2025-04-06T18:52:12.000Z (2 months ago)
- Last Synced: 2025-04-11T16:56:31.657Z (2 months ago)
- Topics: cnn-classification, cnn-for-visual-recognition, cnn-keras, cnn-model, computer-vision, digit-recognition, digit-recognition-mnist, educational, educational-project, image-classification, mnist, mnist-classification, python, tensorflow, tutorial
- Language: Python
- Homepage:
- Size: 12.6 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# MNIST Paint Digit Classifier
A simple convolutional neural network (CNN) built with TensorFlow/Keras to classify handwritten digits using the MNIST dataset. It also includes a tool to recognize digits drawn in external software (like Paint) on a black background with a white number.
This project is designed as a beginner-friendly yet slightly more advanced introduction to convolutional neural networks. It goes beyond the typical minimal examples by including:
- Batch normalization
- ELU activations
- Dropout regularization
More importantly, it focuses on real usability: instead of limiting recognition to internal MNIST test images, it enables direct classification of digits drawn externally, making it practical, visual, and more engaging.
## Who is this for?
- Students starting with AI or deep learning who want to test models with their own images.
- Teachers looking for a quick, visual demo of digit recognition.
- Anyone who wants to train a CNN and classify real-world inputs without setting up complex pipelines or external datasets.
## Project Structure
```
Mnist_numbers/
├── mnist_cnn.py # Script to train the CNN model using MNIST
├── test_model.py # Script to test the model with custom PNG images
├── numberdetector.h5 # Trained model saved in HDF5 format
└── numerospaint/ # Folder containing hand-drawn digit images (PNG format)
├── zero.png
├── one.png
├── ...
└── nine.png
```
## Features
- Trains a CNN on the MNIST dataset with advanced layers.
- Saves the model as an `.h5` file.
- Allows prediction from hand-drawn images (square, grayscale).
### Input Digit Samples
These are all the digits used as input examples from the `numerospaint/` folder:
## Results
- Achieves ~99% accuracy on MNIST test set.
- Achieves 100% accuracy when classifying Paint images with clear digit shapes and correct format.
## Requirements
- Python 3.7+
- TensorFlow
- NumPy
- OpenCV
- Matplotlib
If you need help setting up TensorFlow with GPU support, you can follow this guide:
[TensorFlow GPU Setup by pelayo-felgueroso](https://github.com/pelayo-felgueroso/tensorflow-gpu-setup)
## How to Use
### 1. Train the Model
Run `mnist_cnn.py` to train the CNN and save the model:
```bash
python mnist_cnn.py
```
This generates a file called `numberdetector.h5`.
### 2. Test with Custom Images
Place your hand-drawn digit PNGs (black background, white number, square aspect ratio) into the `numerospaint/` folder. Then run:
```bash
python test_model.py
```
The script will:
- Load the specified image
- Preprocess and resize it to 28x28
- Predict the digit and display the confidence
### Example Output
```
The model predicts the image is the number: 4, with confidence: 0.99987
```
## Image Format Guidelines
- Format: PNG
- Aspect Ratio: Square
- Background: Black
- Digit: White (centered and clearly drawn)
## Author
[Pelayo Felgueroso](https://github.com/pelayo-felgueroso)
---
Feel free to fork, modify, and improve this repository. It's designed for learning and experimentation with computer vision and CNNs using TensorFlow.