一种双判别器GAN的古彝文字符修复方法

A Method of Inpainting Ancient Yi Characters Based on Dual Discriminator Generative Adversarial Networks

在中国,彝文古籍文献日益流失而且损毁严重,由于通晓古彝文的研究人员缺乏,使得古籍恢复工作进展十分缓慢.人工智能在图像文本领域的应用,为古籍文献的自动修复提供可能.本文设计了一种双判别器生成对抗网络(Generative adversarial networks with dual discriminator,D2GAN),以还原古代彝族字符中的缺失部分.D2GAN是在深度卷积生成对抗网络的基础上,增加一个古彝文筛选判别器.通过三个阶段的训练来迭代地优化古彝文字符生成网络,以获得古彝文字符的文字生成器.根据筛选判别器的损失结果优化D2GAN模型,并使用生成的字符恢复古彝文中丢失的笔画.实验结果表明,在字符残缺低于1/3的情况下,本文提出的方法可使文字笔画的修复率达到77.3%,有效地加快了古彝文字符修复工作的进程.

Ancient Yi literatures are increasingly lost and damaged seriously.Due to the lack of ancient Yi researchers,the inpainting of ancient books is progressing very slowly.The application of artificial intelligence is successful in the field of image and texts,so it is possible for automatic inpainting of ancient books.In this paper,a generative adversarial networks with dual discriminator(D2GAN)is designed to restore missing part in ancient Yi characters.The D2GAN is based on the deep convolution generating adversarial network,and adds a selection discriminator.The generation networks of ancient Yi character is optimized iteratively through three-stage training,and the character generator of ancient Yi is established.The loss of selection discriminator is used to optimize the model D2GAN iteratively.So,generated characters based D2GAN can restore the missing stroke in the ancient Yi characters.The experimental results show that the method proposed has an inpainting rate of 77.3%for incomplete characters that the incomplete part does not exceed one third.Therefore,our method is effectively for the inpainting of ancient book,it can accelerate the protection progress of ancient Yi literature.