A Mongolian Handwritten Word Images Generation Approach Based on Generative Adversarial Networks

The particular word formation manner of the Mongolian language makes its vocabulary reach millions. It is tough to manually collect a Mongolian handwritten word images dataset covering all words. In order to realize the automatic generation of Mongolian handwritten word images, an approach based on generative adversarial networks has been proposed in this paper, which is called Mongolian Generator (MG). The proposed MG generates Mongolian handwritten word images with better writing details and more accurate text content. Also, it can generate handwritten word images based on a specific writing style or text content. Moreover, the perceptual adversarial loss has been integrated into the MG, which guarantees that the generated handwritten word images appear more realistic. The experimental results indicate that the performance of the proposed MG outperforms several baselines and state-of-the-art models.