Deep learning-based scene text detection can achieve preferable performance,
powered with sufficient labeled training data. However, manual labeling is time
consuming and laborious. At the extreme, the corresponding annotated data are
unavailable. Exploiting synthetic data is a very promising solution except for
domain distribution mismatches between synthetic datasets and real datasets. To
address the severe domain distribution mismatch, we propose a synthetic-to-real
domain adaptation method for scene text detection, which transfers knowledge
from synthetic data (source domain) to real data (target domain). In this
paper, a text self-training (TST) method and adversarial text instance
alignment (ATA) for domain adaptive scene text detection are introduced. ATA
helps the network learn domain-invariant features by training a domain
classifier in an adversarial manner. TST diminishes the adverse effects of
false positives~(FPs) and false negatives~(FNs) from inaccurate pseudo-labels.
Two components have positive effects on improving the performance of scene text
detectors when adapting from synthetic-to-real scenes. We evaluate the proposed
method by transferring from SynthText, VISD to ICDAR2015, ICDAR2013. The
results demonstrate the effectiveness of the proposed method with up to 10%
improvement, which has important exploration significance for domain adaptive
scene text detection. Code is available at
this https URL

研究了合成数据对实际数据的转化问题，并提出了基于对抗文本实例对齐与文本自训练的技术来解决领域差异，以提高场景文本检测的性能。