计算机科学
人工智能
词(群论)
模式识别(心理学)
生成语法
自然语言处理
计算机视觉
数学
几何学
作者
Hongxi Wei,Kexin Liu,Jing Zhang,Daoerji Fan
标识
DOI:10.1007/978-3-030-86337-1_35
摘要
In order to improve the performance of woodblock printing Mongolian words recognition, a method based on cycle-consistent generative adversarial network (CycleGAN) has been proposed for data augmentation. A well-trained CycleGAN model can learn image-to-image translation without paired examples. To be specific, the style of machine printing word images can be transformed into the corresponding word images with the style of woodblock printing by utilizing a CycleGAN, and vice versa. In this way, new instances of woodblock printing Mongolian word images are able to be generated by using the two generative models of CycleGAN. Thus, the aim of data augmentation could be attained. Given a dataset of woodblock printing Mongolian word images, experimental results demonstrate that the performance of woodblock printing Mongolian words recognition can be improved through such the data augmentation.
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