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