  Localizing text in low-light environments is challenging due to visual
degradations. Although a straightforward solution involves a two-stage pipeline
with low-light image enhancement (LLE) as the initial step followed by
detector, LLE is primarily designed for human vision instead of machine and can
accumulate errors. In this work, we propose an efficient and effective
single-stage approach for localizing text in dark that circumvents the need for
LLE. We introduce a constrained learning module as an auxiliary mechanism
during the training stage of the text detector. This module is designed to
guide the text detector in preserving textual spatial features amidst feature
map resizing, thus minimizing the loss of spatial information in texts under
low-light visual degradations. Specifically, we incorporate spatial
reconstruction and spatial semantic constraints within this module to ensure
the text detector acquires essential positional and contextual range knowledge.
Our approach enhances the original text detector's ability to identify text's
local topological features using a dynamic snake feature pyramid network and
adopts a bottom-up contour shaping strategy with a novel rectangular
accumulation technique for accurate delineation of streamlined text features.
In addition, we present a comprehensive low-light dataset for arbitrary-shaped
text, encompassing diverse scenes and languages. Notably, our method achieves
state-of-the-art results on this low-light dataset and exhibits comparable
performance on standard normal light datasets. The code and dataset will be
released.
