Facilitate manga digital migration by automatically extracting structural lines from patterns.
With the wide popularity of portable devices and the low-cost distribution over the internet, there has been an increasing trend to convert legacy manga to digital form. Comparing to traditional paper-based manga, electronic manga or e-manga is more visually appealing as more visual elements, such as color presentation and powerpoint-like animation, can be easily introduced. During digital migration, extraction of structural lines from pattern-rich manga is a crucial step. Unfortunately, it is very challenging to distinguish structural lines from arbitrary, highly-structured, and black-and-white screen patterns. In this project, we present a novel data-driven approach to identify structural lines out of pattern-rich manga based on convolutional neural networks. Our method can benefit the manga industry in migrating legacy manga to digital manga, which includes a large set of manga-related applications, e.g. manga colorization, manga vectorization, manga retargeting, stereoscopic manga conversion, etc.