  Historically, games of all kinds have often been the subject of study in
scientific works of Computer Science, including the field of machine learning.
By using machine learning techniques and applying them to a game with defined
rules or a structured dataset, it's possible to learn and improve on the
already existing techniques and methods to tackle new challenges and solve
problems that are out of the ordinary. The already existing work on card games
tends to focus on gameplay and card mechanics. This work aims to apply neural
networks models, including Convolutional Neural Networks and Recurrent Neural
Networks, in order to analyze Magic: the Gathering cards, both in terms of card
text and illustrations; the card images and texts are used to train the
networks in order to be able to classify them into multiple categories. The
ultimate goal was to develop a methodology that could generate card text
matching it to an input image, which was attained by relating the prediction
values of the images and generated text across the different categories.
