【摘要】 蒙古文的每个字素在词的不同位置有着不同的书写形式,使得蒙古文字形结构多样且数量庞大,从而导致利用计算机辅助和传统人工方式设计蒙古文字体需要耗费大量的人力物力。故创建一种能自动生成蒙文字体风格的模型十分必要。国内外已有学者开展了对汉字和英文字体风格自动迁移的研究,但蒙古文领域仍处于空白阶段。因此,该文提出将条件生成对抗网络模型应用于蒙古文字体风格迁移,并给出了相关模型,实现了相应的算法和软件。在蒙古文字体数据集上进行实验,模型采用生成损失和判别损失衡量模型,Adam优化器自动调整学习率,逐渐减少差异值,直到生成器和判别器达到纳什平衡状态,可直接从蒙古文标题字体生成蒙古文手写体等字体,得到的生成字体样式基本接近真实字体样式,达到字体风格迁移的效果。更多还原

【Abstract】 Each morpheme of Mongolian has a different writing form at different positions of the word, which makes the structure of Mongolian script glyphs diverse and enormous. As a result, it takes a lot of manpower and material resources to design Mongolian script using computer-assisted or manual methods. This paper proposes the application of conditional generative adversarial network model to Mongolian font style transfer. The model uses generative loss and discriminative loss measurement models. Adam Optimizer automatically adjusts the learning rate and gradually reduces the difference until the generator and discriminator reach the Nash equilibrium state. Experimented on the Mongolian font data set, it can be observed that Mongolia can be generated directly from the Mongolian title font, and the generated fonts are basically similar to the real font styles.更多还原