  The requiring of large amounts of annotated training data has become a common
constraint on various deep learning systems. In this paper, we propose a weakly
supervised scene text detection method (WeText) that trains robust and accurate
scene text detection models by learning from unannotated or weakly annotated
data. With a "light" supervised model trained on a small fully annotated
dataset, we explore semi-supervised and weakly supervised learning on a large
unannotated dataset and a large weakly annotated dataset, respectively. For the
unsupervised learning, the light supervised model is applied to the unannotated
dataset to search for more character training samples, which are further
combined with the small annotated dataset to retrain a superior character
detection model. For the weakly supervised learning, the character searching is
guided by high-level annotations of words/text lines that are widely available
and also much easier to prepare. In addition, we design an unified scene
character detector by adapting regression based deep networks, which greatly
relieves the error accumulation issue that widely exists in most traditional
approaches. Extensive experiments across different unannotated and weakly
annotated datasets show that the scene text detection performance can be
clearly boosted under both scenarios, where the weakly supervised learning can
achieve the state-of-the-art performance by using only 229 fully annotated
scene text images.
