  The latest trend in the bottom-up perspective for arbitrary-shape scene text
detection is to reason the links between text segments using Graph
Convolutional Network (GCN). Notwithstanding, the performance of the best
performing bottom-up method is still inferior to that of the best performing
top-down method even with the help of GCN. We argue that this is not mainly
caused by the limited feature capturing ability of the text proposal backbone
or GCN, but by their failure to make a full use of visual-relational features
for suppressing false detection, as well as the sub-optimal route-finding
mechanism used for grouping text segments. In this paper, we revitalize the
classic text detection frameworks by aggregating the visual-relational features
of text with two effective false positive/negative suppression mechanisms.
First, dense overlapping text segments depicting the `characterness' and
`streamline' of text are generated for further relational reasoning and weakly
supervised segment classification. Here, relational graph features are used for
suppressing false positives/negatives. Then, to fuse the relational features
with visual features, a Location-Aware Transfer (LAT) module is designed to
transfer text's relational features into visual compatible features with a Fuse
Decoding (FD) module to enhance the representation of text regions for the
second step suppression. Finally, a novel multiple-text-map-aware
contour-approximation strategy is developed, instead of the widely-used
route-finding process. Experiments conducted on five benchmark datasets, i.e.,
CTW1500, Total-Text, ICDAR2015, MSRA-TD500, and MLT2017 demonstrate that our
method outperforms the state-of-the-art performance when being embedded in a
classic text detection framework, which revitalises the superb strength of the
bottom-up methods.
