  Regression-based text detection methods have already achieved promising
performances with simple network structure and high efficiency. However, they
are behind in accuracy comparing with recent segmentation-based text detectors.
In this work, we discover that one important reason to this case is that
regression-based methods usually utilize a fixed feature selection way, i.e.
selecting features in a single location or in neighbor regions, to predict
components of the bounding box, such as the distances to the boundaries or the
rotation angle. The features selected through this way sometimes are not the
best choices for predicting every component of a text bounding box and thus
degrade the accuracy performance. To address this issue, we propose a novel
Location-Aware feature Selection text detection Network (LASNet). LASNet
selects suitable features from different locations to separately predict the
five components of a bounding box and gets the final bounding box through the
combination of these components. Specifically, instead of using the
classification score map to select one feature for predicting the whole
bounding box as most of the existing methods did, the proposed LASNet first
learn five new confidence score maps to indicate the prediction accuracy of the
bounding box components, respectively. Then, a Location-Aware Feature Selection
mechanism (LAFS) is designed to weightily fuse the top-$K$ prediction results
for each component according to their confidence score, and to combine the all
five fused components into a final bounding box. As a result, LASNet predicts
the more accurate bounding boxes by using a learnable feature selection way.
The experimental results demonstrate that our LASNet achieves state-of-the-art
performance with single-model and single-scale testing, outperforming all
existing regression-based detectors.
