Handwritten-digit-recognition-MNIST

Handwritten Digit Recognition using Convolutional Neural Networks in Python with Keras

MNIST dataset:

MNIST is a collection of handwritten digits from 0-9. Image of size 28 X 28

Description:

This is a 8 layers Sequential Convolutional Neural Network for digits recognition trained on MNIST dataset. I choosed to build it with keras API (Tensorflow backend) which is very intuitive.

It achieved 84.57% of accuracy with this CNN trained on a CPU, which took me about a 5-10minute. If you dont have a GPU powered machine it might take a little longer, you can try reducing the epochs (steps) to reduce computation.

Code Requirements:

python 3.x with following modules installed

1. numpy
2. keras
3. opencv2

Execution:

pip install -r requirements.txt

python model.py

python run.py

Built With

• Keras - Keras is a deep learning API
• Opencv2 - open-source computer vision library

Purpose

The purpose of this project was to gain introductory exposure to Deep Learning Classification concepts. The project makes heavy use of Keras(tensorflow in backend) and Opencv2 Libraries.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

License

MIT