Scene Text Editing based on Style Transfer

 In order to meet the requirements that the non-professional users can quickly edit characters on images and simultaneously keep the geometric and visual consistency with adjacent characters as much as possible, a scene image text editing method is proposed. First of all, in order to solve the problems existing in the original character color transfer dataset, such as unreasonable color distribution, lack of character combinations, dirty data, etc., the color-list is reconstructed, and a dataset expansion method based on efficient semantic segmentation is adopted to expand the character combinations and color-lists in the dataset. We build a character color transfer image(CCT) dataset and the character generation effects of different models are evaluated on the test dataset. The experimental results show that the character color transfer model trained by the CCT dataset can greatly improve the image quality (10.47% in SSIM and 7.90% in PSNR) compared with existing network model. Finally, the user study further verifies the effectiveness of the method and the fidelity of the generated image.