ASCII Art Classification Model by Transfer Learning and Data Augmentation

Abstract

In this study, we propose an ASCII art category classification method based on transfer learning and data augmentation. ASCII art is a form of nonverbal expression that visually expresses emotions and intentions. While there are similar expressions such as emoticons and pictograms, most are either represented by a single character or are embedded in the statement as an inline expression. ASCII art is expressed in various styles, including dot art illustration and line art illustration. Basically, ASCII art can represent almost any object, and therefore the category of ASCII art is very diverse. Many existing image classification algorithms use color information; however, since most ASCII art is written in character sets, there is no color information available for categorization. We created an ASCII art category classifier using the grayscale edge image and the ASCII art image transformed from the image as a training image set. We also used VGG16, ResNet-50, Inception v3, and Xception’s pre-trained networks to fine-tune our categorization. As a result of the experiment of fine tuning by VGG16 and data augmentation, an accuracy rate of 80% or more was obtained in the “human” category.