  Manga is a fashionable Japanese-style comic form that is composed of
black-and-white strokes and is generally displayed as raster images on digital
devices. Typical mangas have simple textures, wide lines, and few color
gradients, which are vectorizable natures to enjoy the merits of vector
graphics, e.g., adaptive resolutions and small file sizes. In this paper, we
propose MARVEL (MAnga's Raster to VEctor Learning), a primitive-wise approach
for vectorizing raster mangas by Deep Reinforcement Learning (DRL). Unlike
previous learning-based methods which predict vector parameters for an entire
image, MARVEL introduces a new perspective that regards an entire manga as a
collection of basic primitives\textemdash stroke lines, and designs a DRL model
to decompose the target image into a primitive sequence for achieving accurate
vectorization. To improve vectorization accuracies and decrease file sizes, we
further propose a stroke accuracy reward to predict accurate stroke lines, and
a pruning mechanism to avoid generating erroneous and repeated strokes.
Extensive subjective and objective experiments show that our MARVEL can
generate impressive results and reaches the state-of-the-art level. Our code is
open-source at: https://github.com/SwordHolderSH/Mang2Vec.
