Machine Learning Engineer Nanodegree

Capstone Project: Handwritten Digit Recognition using Deep Neural Network

Install

This project requires Python 2.7 and the following Python libraries installed:

• Anaconda2
• Tensorflow or Theano
• Keras
• OpenCV
• (Optional) A GPU with Cuda support is highly recommended for fast computation. Install respective graphics drivers, cuda toolkit, cudnn, tensorflow-gpu version

You will also need to have software installed to run and execute a Jupyter Notebook

If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included. Make sure that you select the Python 2.7 installer and not the Python 3.x installer.

Code

The code is provided in the handwritten_digit_recognizer.ipynb notebook file. The MNIST dataset required for this project will be downloaded and loaded in the code itself. No need to download manually.

Run

In a terminal or command window, navigate to the top-level project directory Udacity-MLND-Capstone-Handwritting-Digit-Recognition/ (that contains this README) and run one of the following commands:

ipython notebook handwritten_digit_recognizer.ipynb

or

jupyter notebook handwritten_digit_recognizer.ipynb

This will open the Jupyter Notebook software and project file in your browser.

Data

The MNIST dataset required for this project will be downloaded and loaded in the code itself. No need to download manually. This dataset has the following description:

The MNIST database of handwritten digits has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image.

The original black and white (bi-level) images from NIST were size normalized to fit in a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels as a result of the anti-aliasing technique used by the normalization algorithm. the images were centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field.

The dataset can be downloaded and loaded using Keras, a high-level neural networks python library using the following code

from	keras.datasets	import	mnist	
(X_train,	y_train),	(X_test,	y_test)	=	mnist.load_data()	

• X_train,X_test: uint8 array of grayscale image split as train and test data
• y_train,y_test: uint8 array of digit labels (integers in range 0-9) split as train and test data

Thus we can use the above variables to train the model with pixel-values of the handwritten image as features(X_train) and its respective label(y_train) as target. We can also test the model by evaluating it against the test variables X_test and y_test.