This project implements a machine learning–based handwritten digit classifier capable of recognizing digits from 0 to 9. The model is trained on the widely used MNIST dataset and demonstrates the ML workflow—from data preprocessing and model training to evaluation and prediction.
The goal of this project is to build a reliable baseline digit recognition system while following clean coding practices and proper version control.
Handwritten digit recognition is a classic computer vision and pattern recognition problem. Variations in handwriting styles, stroke thickness, and digit alignment make classification difficult. This project approaches the problem using supervised machine learning techniques.
- Dataset: MNIST
- Training samples: 60,000
- Test samples: 10,000
- Image size: 28 × 28 pixels (greyscale)
- Classes: Digits from 0 to 9
- Loaded and explored the MNIST dataset
- Normalization and preprocessing of data (Images)
- Built a machine learning model for multiclass classification
- Trained the model using the preprocessed and labeled dataset
- Evaluated performance on unseen test data set
- Python
- NumPy
- TensorFlow and keras
- Clone the repository
git clone https://github.com/BindhuC06/Digit-Classifier-Model.git
- Install dependencies
pip install -r requirements.txt
- Run the model
python main.py