Recognizing the Order of Four-scene Comics by Three-Path Convolutional Neural Networks

Abstract

Recently, the comic analysis has become an attractive research topic in the artificial intelligence fields as comic engineering. In this study, we focused on the four-scene comics and applied deep convolutional neural networks (DCNNs) to those data for understanding the order structure. We proposed the novel approach for that problem by three-path DCNN with special input data formats. The hyperparameters of three-path DCNN are obtained by evolutionary deep learning (evoDL). The effectiveness of the proposed method is confirmed by computer simulations taking a real four-scene comics structure recognition problem as an example.