Recurrent Neural Networks for drawing classification

The model used in this chapter was trained on the dataset used in Google's AI experiment Quick, Draw!

Quick, Draw! is a game where players are challenged to draw a given object to see whether the computer can recognize it; an extract of the data is shown as follows:

The technique was inspired from the work done on handwritten recognition (Google Translate), where, rather than looking at the image as a whole, the team worked with data features describing how the characters were drawn. This is illustrated in the following image:

Source: https://experiments.withgoogle.com/ai/quick-draw

The hypothesis here is that there exists some consistent pattern of how people draw certain types of objects; but to discover those patterns, we would need a lot of data, which we do have. The dataset consists of over 50 million drawings across 345 categories obtained cleverly, from the players of the Quick, Draw! game. Each sample is described with timestamped...