Text Line Adjustment Based on Neural Network

With the development of artificial intelligence technology, many text generation models have emerged. People generate single words in various ways. For example, different forms of English characters and numbers can be generated through Generative Adversarial Network (GAN). Then, text lines can be synthesized from single words. However, most people only use some well-defined rules to synthesize "text line" from "single word". The result depends on people's experience. Therefore, the generated text lines often seem inconsistent, especially when generating handwriting text line. For example, it's hard to make the adjacent parts of the text look real. Moreover, adjacent words may have different styles. These problems can not be solved by simply modifying the text synthesis program. In this paper, we attempts to use neural networks to adjust the synthesized text lines. The input of our model is a synthetic text line, and then it can output a more coordinated text line. Four different loss and a discriminator ensure the quality of the generated text lines. In experiments, our neural network has successfully corrected many unnatural aspects of synthetic text lines. Considering that no one has tried to adjust the word at the text line level, our experiment offers novel and valuable insights.