  Irregular-shaped texts bring challenges to Scene Text Detection (STD).
Although existing contour point sequence-based approaches achieve comparable
performances, they fail to cover some highly curved ribbon-like text lines. It
leads to limited text fitting ability and STD technique application.
Considering the above problem, we combine text geometric characteristics and
bionics to design a natural leaf vein-based text representation method (LVT).
Concretely, it is found that leaf vein is a generally directed graph, which can
easily cover various geometries. Inspired by it, we treat text contour as leaf
margin and represent it through main, lateral, and thin veins. We further
construct a detection framework based on LVT, namely LeafText. In the text
reconstruction stage, LeafText simulates the leaf growth process to rebuild
text contour. It grows main vein in Cartesian coordinates to locate text
roughly at first. Then, lateral and thin veins are generated along the main
vein growth direction in polar coordinates. They are responsible for generating
coarse contour and refining it, respectively. Considering the deep dependency
of lateral and thin veins on main vein, the Multi-Oriented Smoother (MOS) is
proposed to enhance the robustness of main vein to ensure a reliable detection
result. Additionally, we propose a global incentive loss to accelerate the
predictions of lateral and thin veins. Ablation experiments demonstrate LVT is
able to depict arbitrary-shaped texts precisely and verify the effectiveness of
MOS and global incentive loss. Comparisons show that LeafText is superior to
existing state-of-the-art (SOTA) methods on MSRA-TD500, CTW1500, Total-Text,
and ICDAR2015 datasets.
