#the Handwritten Digit Recognition using LeNet-5 Architecture and Streamlit

This is a project that recognizes handwritten digits using the LeNet-5 architecture and deploys the model using Streamlit, a popular Python library for building interactive web applications.

Requirements

• Python 3.x
• TensorFlow
• Streamlit
• Numpy
• OpenCv

Installation

1. Clone the repository:

   git clone https://github.com/ahmedhassan187/digit-recognizer
   

2. Install the required packages:

   pip install -r requirements.txt
   

3. Run the application:

   streamlit run app.py
   

4. Open the URL http://localhost:8501 in your browser to access the web application.

Usage

1. Upload a photo of a handwritten digit to the web application.

2. The LeNet-5 model will then predict the digit and display the result on the screen.

Website Link

Website

Files

• app.py: This is the main file that contains the code for deploying the model using Streamlit.

• model.ipynb: This file contains the implementation of the LeNet-5 architecture.

• Utilites.py: This file contains the helper functions used for loading and preprocessing the input image.

• model.h5: This is the trained model that is used for predicting the digit.

• requirements.txt: This file contains the list of required packages and their versions.

Acknowledgements

This project was made possible thanks to the following resources:

• MNIST dataset Digits
• LeNet-5 Paper
• Streamlit documentation