/digit-recognizer

This project involves deploying a model that recognizes handwritten numbers using the Lenet-5 architecture. The Lenet-5 architecture is a convolutional neural network (CNN) that was introduced in the 1990s and was one of the first CNNs to achieve high accuracy on the MNIST dataset, which is a commonly used benchmark for image classification.

Primary LanguageJupyter Notebook

#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: