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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, evaluationprotocols, etc.). These various settings would dissemble the pros and cons ofthe proposed core techniques. In this paper, we carefully examine and analyzethe inconsistent settings, and propose a unified framework for the bottom-upbased scene text detection methods. Under the unified framework, we ensure theconsistent settings for non-core modules, and mainly investigate therepresentations of describing arbitrary-shape scene texts, e.g., regressingpoints on text contours, clustering pixels with predicted auxiliaryinformation, grouping connected components with learned linkages, etc. With thecomprehensive investigations and elaborate analyses, it not only cleans up theobstacle of understanding the performance differences between existing methodsbut also reveals the advantages and disadvantages of previous models under faircomparisons.

Comprehensive Studies for Arbitrary-shape Scene Text Detection