A Human Eye-based Text Color Scheme Generation Method for Image Synthesis

TL;DR

This paper introduces a novel human eye-inspired color scheme generation method for image synthesis that improves text-background distinction, allows text across depths, and operates at high speed, enhancing scene text detection data quality.

Contribution

The proposed method innovatively models human eye perception to generate more effective and versatile text color schemes for synthetic images, surpassing existing fixed or depth-analyzing approaches.

Findings

Outperforms state-of-the-art methods in text-background distinction.

Enables text placement across various image depths.

Generates images at nearly three milliseconds per picture.

Abstract

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.