Fast Instance Segmentation for Line Drawing Vectorization

Naoto Inoue, Toshihiko Yamasaki

Abstract: In this paper, we present a fast raster-to-vector method for line art images based on a convolutional neural network (CNN). State-of-the-art approaches for vectorization are very slow because they mostly consist of multiple steps including iterative optimization during the inference. In contrast, our model is based on a simple CNN extending a proposal-based instance segmentation algorithm. Therefore, it is very fast and end-to-end trainable. We experimentally show that our model is about 100 times - 1000 times faster than the previous vectorization methods without sacrificing accuracy very much.