Fast Semantic Segmentation for Vectorization of Line Drawings Based on Deep Neural Networks

Abstract

Much research has been done on pattern recognition in line drawings. Converting raster graphics into vector graphics is one such examples. Vector graphics are composed of meaningful basic components such as lines, curves, and parabolas etc. However, converting raster graphic to a vector graphic is difficult because the structures of the basic components must be recognized. Therefore, we propose a semantic segmentation method for converting line drawings in raster format into vector format and verify the accuracy of the extraction of basic components and the processing time through computer experiments.