Converting graphs to ASCII art with convolutional neural network
Jiongyi Li · Applied and Computational Engineering · 2024
In an era marked by data's rapid proliferation, novel data representation and analysis methods have become increasingly significant. However, translating complex graphical structures into character-based representations remains a largely unexplored territory. This problem holds considerable importance due to its potential applications in fields like data compression and the development of innovative graphical interfaces. This study seeks to address this gap by proposing a unique methodology that uses a Convolutional Neural Network (CNN) model to translate graphical images into corresponding character arrangements. The approach involves preprocessing graphical inputs using edge detection techniques, slicing the pre-processed graph into specific columns, and feeding the resulting slices into the trained Convolutional Neural Network (CNN) model for character prediction. I interpret the SoftMax output of the model to determine the most probable character for each slice. The results indicate that the granularity of slicing impacts the accuracy of the generated character-laden graph, with higher granularity producing more precise translations. This finding demonstrates the model's ability to effectively translate graphical data into character-based representations, offering promising prospects for future study in this domain.