Hand-Written Digit Recognition System

Project Overview

This project is designed to recognize hand-written digits (0-9) from an input image. The system uses machine learning algorithms to predict the digit from the given hand-written image. It is a prototype for academic purposes, showcasing how image classification can be applied in the field of machine learning.

System Requirements

• Anaconda Environment with Python 3.10 version
• Required Libraries: All dependencies are listed in the requirements.txt file.

Installation Instructions

1. Create a Conda environment with Python 3.10:

   conda create --name handwritten-digit-recognition python=3.10

2. Activate the Conda environment:

   conda activate handwritten-digit-recognition

3. Install the required dependencies:

   pip install -r requirements.txt

4. Run the prediction script:

   python prediction.py

   The script will allow you to input hand-written digit images for recognition and display the predicted digit.

Dataset Information

The dataset used for training the model is available on Kaggle. It contains images of hand-written digits from 0 to 9, allowing the model to learn the patterns of hand-written numbers and make accurate predictions.

• Dataset source: The dataset is available on Kaggle (no direct link provided).

Credits

• Dataset: The dataset for training the model is sourced from Kaggle.
• This project was developed for academic purposes to demonstrate digit recognition using machine learning techniques.

License

This project is intended for academic purposes only. It is not permitted to use this project for commercial purposes without proper authorization.