  Existing real-time text detectors reconstruct text contours by shrink-masks
directly, which simplifies the framework and can make the model run fast.
However, the strong dependence on predicted shrink-masks leads to unstable
detection results. Moreover, the discrimination of shrink-masks is a pixelwise
prediction task. Supervising the network by shrink-masks only will lose much
semantic context, which leads to the false detection of shrink-masks. To
address these problems, we construct an efficient text detection network,
Adaptive Shrink-Mask for Text Detection (ASMTD), which improves the accuracy
during training and reduces the complexity of the inference process. At first,
the Adaptive Shrink-Mask (ASM) is proposed to represent texts by shrink-masks
and independent adaptive offsets. It weakens the coupling of texts to
shrink-masks, which improves the robustness of detection results. Then, the
Super-pixel Window (SPW) is designed to supervise the network. It utilizes the
surroundings of each pixel to improve the reliability of predicted shrink-masks
and does not appear during testing. In the end, a lightweight feature merging
branch is constructed to reduce the computational cost. As demonstrated in the
experiments, our method is superior to existing state-of-the-art (SOTA) methods
in both detection accuracy and speed on multiple benchmarks.
