An experiment in generating ASCII Art from images with an artificial neural network.

IN PROGRESS

Currently only a hard-coded test image is converted:

$ python test_ocr.py

Source:

Generated:

This experiment trains a neural network model as an optical character recognizer.

The model is a simple 2 layer neural network:

• 99 inputs (glyphs in the font are 9x11 pixels)
• fully connected hidden layer with 99 nodes with bias and sigmoid activation
• fully connected output layer with 92 nodes (number of different characters used) with bias and softmax activation
• cross entrophy loss function

It is trained with rendered glyphs of a font and corresponding labels as inputs, e.g.:

a	b	#

And uses those to predict the best label for the tiles in an input image, e.g.:

_	,	#	*	7

Two implementations of the model were made:

This is my own implementation of MLPs using only numpy arrays. It supports batch learning with stochastic gradient descent.

Developing this has been a great help in understanding the maths behind the back propagation algorithm and why it is so efficient for calculating the loss derivates necessary for gradient descent. I highly recommend the blog post Calculus on Computational Graphs: Backpropagation for further reading.

This is an implementation using the Keras Deep Learning Library. It is trained using stochastic gradient descent with Nesterov momentum.

Training this model is quite a bit faster then for my numpy implementation and really simple to implement.

• Use deep convolutional neural nets to detect more abstract features for better selection of ASCII character
• generate more general training data by randomly transforming the glyphs (translation, scale, shear)
• use overlapping segments of input image to include surrounding pixels into selection of ASCII character