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 welldefined rules to synthesize text line from single word. The result depends on peopleu0027s experience. Therefore, the generated text lines often seem inconsistent, especially when generating handwriting text line. For example, itu0027s 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.

Adversarial Generation of Handwritten Text Images Conditioned on Sequences We propose a system based on Generative Adversarial Networks (GAN) to produce synthetic images of handwritten words. We use bidirectional LSTM recurrent layers to get an embedding of the word to be rendered, and we feed it to the generator network. We also modify the standard GAN by adding an auxiliary network for text recognition. The system is then trained with a balanced combination of an adversarial loss and a CTC loss. Together, these extensions to GAN enable to control the textual content of the generated word images yielding realisticlooking images on both French and Arabic languages. Stateoftheart offline handwriting text recognition systems tend to use neural networks and therefore require a large amount of annotated data to be trained. In order to partially satisfy this requirement, we could use those synthetic images to increase the amount of training data. We show that integrating generated images into the existing training data of a text recognition system can slightly enhance its performance.