  Numerous scene text detection methods have been proposed in recent years.
Most of them declare they have achieved state-of-the-art performances. However,
the performance comparison is unfair, due to lots of inconsistent settings
(e.g., training data, backbone network, multi-scale feature fusion, evaluation
protocols, etc.). These various settings would dissemble the pros and cons of
the proposed core techniques. In this paper, we carefully examine and analyze
the inconsistent settings, and propose a unified framework for the bottom-up
based scene text detection methods. Under the unified framework, we ensure the
consistent settings for non-core modules, and mainly investigate the
representations of describing arbitrary-shape scene texts, e.g., regressing
points on text contours, clustering pixels with predicted auxiliary
information, grouping connected components with learned linkages, etc. With the
comprehensive investigations and elaborate analyses, it not only cleans up the
obstacle of understanding the performance differences between existing methods
but also reveals the advantages and disadvantages of previous models under fair
comparisons.
