Improvement of Line Drawing Vectorization Based on Semantic Segmentation Using Neural Networks

Abstract

Vector graphics are composed of meaningful basic components such as lines, curves, and parabolas etc. Therefore, in vector graphics, it is easy to edit each basic component such as resizing, translation, and rotation etc. Because of these advantages, the international e-book file format EPUB recommends saving images in the SVG format. On the other hand, most images are saved electronically in raster format or exist as printed materials. Since an image drawn in raster format is simply a set of pixels, it is not easy to divide the image into its basic components. Therefore, we propose a semantic segmentation method for converting line drawings in raster format into vector format and verify its effectiveness through computer experiments. The model proposed in this paper outperforms a conventional model in terms of both extraction accuracy and computational processing speed.