  Synthetic data used for scene text detection and recognition tasks have
proven effective. However, there are still two problems: First, the color
schemes used for text coloring in the existing methods are relatively fixed
color key-value pairs learned from real datasets. The dirty data in real
datasets may cause the problem that the colors of text and background are too
similar to be distinguished from each other. Second, the generated texts are
uniformly limited to the same depth of a picture, while there are special cases
in the real world that text may appear across depths. To address these
problems, in this paper we design a novel method to generate color schemes,
which are consistent with the characteristics of human eyes to observe things.
The advantages of our method are as follows: (1) overcomes the color confusion
problem between text and background caused by dirty data; (2) the texts
generated are allowed to appear in most locations of any image, even across
depths; (3) avoids analyzing the depth of background, such that the performance
of our method exceeds the state-of-the-art methods; (4) the speed of generating
images is fast, nearly one picture generated per three milliseconds. The
effectiveness of our method is verified on several public datasets.
