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Recent scene text detection methods are almost based on deep learning anddata-driven. Synthetic data is commonly adopted for pre-training due toexpensive annotation cost. However, there are obvious domain discrepanciesbetween synthetic data and real-world data. It may lead to sub-optimalperformance to directly adopt the model initialized by synthetic data in thefine-tuning stage. In this paper, we propose a new training paradigm for scenetext detection, which introduces an \textbf{UN}supervised \textbf{I}ntermediate\textbf{T}raining \textbf{S}tage (UNITS) that builds a buffer path toreal-world data and can alleviate the gap between the pre-training stage andfine-tuning stage. Three training strategies are further explored to perceiveinformation from real-world data in an unsupervised way. With UNITS, scene textdetectors are improved without introducing any parameters and computationsduring inference. Extensive experimental results show consistent performanceimprovements on three public datasets.

UNITS: Unsupervised Intermediate Training Stage for Scene Text Detection