[2Z3] [General Session] 5. Web Intelligence

[2Z3-04] Comparison of Classification Methods for Ascii Art

〇Kazuyuki Matsumoto¹, Akira Fujisawa², Minoru Yoshida¹, Kenji Kita¹ (1. Tokushima University, 2. Aomori University)

Keywords:Ascii Art, image feature, character feature, neural networks

In recent years, a lot of non-verbal expressions have been used on social media. Ascii art (AA) is an expression by visual technique using characters. In this paper, we set up an experiment to classify AA pictures by using character features and image features. We try to clear which feature is more effective for the method to classify AA pictures. We proposed four methods; 1) character frequency based method, 2) character importance value based method and 3) image feature based method, 4) character's image feature based method. We trained the neural networks by using these four features. As the experimental result, the best classification accuracy was obtained with the feed forward neural networks using character's image feature.