  Arbitrary-shaped scene text detection is a challenging task due to the
variety of text changes in font, size, color, and orientation. Most existing
regression based methods resort to regress the masks or contour points of text
regions to model the text instances. However, regressing the complete masks
requires high training complexity, and contour points are not sufficient to
capture the details of highly curved texts. To tackle the above limitations, we
propose a novel light-weight anchor-free text detection framework called
TextDCT, which adopts the discrete cosine transform (DCT) to encode the text
masks as compact vectors. Further, considering the imbalanced number of
training samples among pyramid layers, we only employ a single-level head for
top-down prediction. To model the multi-scale texts in a single-level head, we
introduce a novel positive sampling strategy by treating the shrunk text region
as positive samples, and design a feature awareness module (FAM) for
spatial-awareness and scale-awareness by fusing rich contextual information and
focusing on more significant features. Moreover, we propose a segmented
non-maximum suppression (S-NMS) method that can filter low-quality mask
regressions. Extensive experiments are conducted on four challenging datasets,
which demonstrate our TextDCT obtains competitive performance on both accuracy
and efficiency. Specifically, TextDCT achieves F-measure of 85.1 at 17.2 frames
per second (FPS) and F-measure of 84.9 at 15.1 FPS for CTW1500 and Total-Text
datasets, respectively.
