Skip to content

This repository contains a deep learning project that utilizes Convolutional Neural Networks (CNN) to build a digit recognizer using the MNIST dataset.

License

Notifications You must be signed in to change notification settings

TheStrange-007/DigitRecognizer

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

5 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

CNN Digit Recognizer 🔢

This repository houses an exciting deep learning project employing Convolutional Neural Networks (CNN) to construct a digit recognizer using the MNIST dataset. The MNIST dataset comprises a vast collection of handwritten digit images.

DigitRecognizer

Dependencies 🛠️

Ensure you have the following packages installed with the specified versions:

  • numpy
  • matplotlib
  • tensorflow
  • keras
  • pillow or opencv-python

Usage 🚀

  1. Clone the repository to your local machine:

    git clone https://github.com/TheStrange-007/DigitRecognizer.git
  2. Install required packages:

    pip install -r requirements.txt
  3. Run the Flask server:

    python server.py
  4. Access the web interface:

    Visit http://localhost:5000 in your web browser.

  5. Draw a digit:

    In the provided interface, draw a digit, and the trained model will predict the corresponding digit.

🌐 Alternatively, use the pre-trained model:

Access the model at https://thestrange-007.github.io/DigitRecognizer/.

Model Training 🧠

The repository includes code for training the CNN model using the MNIST dataset. The model architecture comprises convolutional layers, pooling layers, dropout layers, and dense layers. Augmentation techniques are applied to generate augmented images for training.

License 📜

This project is licensed under the MIT License.

About

This repository contains a deep learning project that utilizes Convolutional Neural Networks (CNN) to build a digit recognizer using the MNIST dataset.

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published