  Most existing scene text detectors require large-scale training data which
cannot scale well due to two major factors: 1) scene text images often have
domain-specific distributions; 2) collecting large-scale annotated scene text
images is laborious. We study domain adaptive scene text detection, a largely
neglected yet very meaningful task that aims for optimal transfer of labelled
scene text images while handling unlabelled images in various new domains.
Specifically, we design SCAST, a subcategory-aware self-training technique that
mitigates the network overfitting and noisy pseudo labels in domain adaptive
scene text detection effectively. SCAST consists of two novel designs. For
labelled source data, it introduces pseudo subcategories for both foreground
texts and background stuff which helps train more generalizable source models
with multi-class detection objectives. For unlabelled target data, it mitigates
the network overfitting by co-regularizing the binary and subcategory
classifiers trained in the source domain. Extensive experiments show that SCAST
achieves superior detection performance consistently across multiple public
benchmarks, and it also generalizes well to other domain adaptive detection
tasks such as vehicle detection.
