Neural Networks Models for Analyzing Magic: the Gathering Cards

TL;DR

This paper explores the application of neural network models, including CNNs and RNNs, to analyze Magic: the Gathering cards' text and images, aiming to classify and generate card text based on visual input.

Contribution

It introduces a methodology combining neural networks to classify card features and generate matching card text from images, advancing card analysis techniques.

Findings

Neural networks successfully classify card images and texts.

The approach enables generating card text from visual features.

Methodology bridges image analysis and text generation for cards.

Abstract

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.