  Segmentation-based methods are widely used for scene text detection due to
their superiority in describing arbitrary-shaped text instances. However, two
major problems still exist: 1) current label generation techniques are mostly
empirical and lack theoretical support, discouraging elaborate label design; 2)
as a result, most methods rely heavily on text kernel segmentation which is
unstable and requires deliberate tuning. To address these challenges, we
propose a human cognition-inspired framework, termed, Conceptual Text Region
Network (CTRNet). The framework utilizes Conceptual Text Regions (CTRs), which
is a class of cognition-based tools inheriting good mathematical properties,
allowing for sophisticated label design. Another component of CTRNet is an
inference pipeline that, with the help of CTRs, completely omits the need for
text kernel segmentation. Compared with previous segmentation-based methods,
our approach is not only more interpretable but also more accurate.
Experimental results show that CTRNet achieves state-of-the-art performance on
benchmark CTW1500, Total-Text, MSRA-TD500, and ICDAR 2015 datasets, yielding
performance gains of up to 2.0%. Notably, to the best of our knowledge, CTRNet
is among the first detection models to achieve F-measures higher than 85.0% on
all four of the benchmarks, with remarkable consistency and stability.
