  The requirement of large amounts of annotated images has become one grand
challenge while training deep neural network models for various visual
detection and recognition tasks. This paper presents a novel image synthesis
technique that aims to generate a large amount of annotated scene text images
for training accurate and robust scene text detection and recognition models.
The proposed technique consists of three innovative designs. First, it realizes
"semantic coherent" synthesis by embedding texts at semantically sensible
regions within the background image, where the semantic coherence is achieved
by leveraging the semantic annotations of objects and image regions that have
been created in the prior semantic segmentation research. Second, it exploits
visual saliency to determine the embedding locations within each semantic
sensible region, which coincides with the fact that texts are often placed
around homogeneous regions for better visibility in scenes. Third, it designs
an adaptive text appearance model that determines the color and brightness of
embedded texts by learning from the feature of real scene text images
adaptively. The proposed technique has been evaluated over five public datasets
and the experiments show its superior performance in training accurate and
robust scene text detection and recognition models.
