Digit Recognition Model README

See live website at : https://digit-recognition-benab04.streamlit.app/

Overview

This repository contains the implementation of a digit recognition model using a neural network with three layers. The model is designed to recognize handwritten digits and can be utilized for various applications such as digit classification.

Model Architecture

The digit recognition model is built with three layers:

1. Input Layer: The input layer represents the flattened pixel values of the input images. Each pixel serves as a feature for the model.

2. Hidden Layer: The hidden layer is responsible for learning complex patterns and representations from the input data. It enhances the model's ability to recognize features in the images.

3. Output Layer: The output layer produces the final prediction. For digit recognition, this layer typically has 10 neurons corresponding to digits 0 through 9, and the softmax activation function is commonly used to convert raw scores into probability distributions.

Requirements

• Python 3.11
• Dependencies listed in the requirements.txt file.

Usage

1. Installation:

   pip install -r requirements.txt