A Method of Inpainting Ancient Yi Characters Based on Dual Discriminator Generative Adversarial Networks
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摘要: 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.Abstract: 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.
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Key words:
- Yi characters /
- generativeadversarialnetwork(GAN) /
- deeplearning /
- gradientdescent
1) 收稿日期 2019-10-30 录用日期 2020-04-16 Manuscript received October 30, 2019; accepted April 16, 2020 国家自然科学基金 (61603310), 国家社会科学基金 (19BYY171), 重庆市自然科学基金 (cstc2019jcyj-msxm2550), 模式识别国家重点实验室开放课题 (201900010), 中央高校基本科研业务费(XDJK2018B020), 重庆市教育委员会科学技术研究计划青年项目(KJQN201801901, KJQN201801902) 资助 Supported by National Natural Science Foundation of China (61603310), National Social Science Foundation of China (19BYY171), Natural Science Foundation of Chongqing (cstc2019jcyj-msxm2550), Open Projects Program of National Laboratory of Pattern Recognition (201900010), Fundamental Research Funds for the Central Universities of China (XDJK2018B020), and Youth Project of Science and Technology Research Program of Chongqing Education Commission (KJQN201801901, KJQN201801902) 本文责任编委 金连文 Recommended by Associate Editor JIN Lian-Wen 1. 西南大学计算机与信息科学学院 重庆 400715 2. 重庆工程学院计算机与物联网学院 重庆 400056 3. 贵州工程应用技术学2) 院彝学研究院 毕节 551700 1. College of Computer and Information Science, Southwest University, Chongqing 400715 2. College of Computer and Internet of Things, Chongqing Institute of Engineering College, Chongqing 400056 3. Institute of Yi Studies, Guizhou University of Engineering Science, Bijie 551700 -
图 2 本文双判别器生成式对抗网络结构
Fig. 2 Generative adversarial networks with double discriminator in the paper
图 5 古彝文字符判别器模型详细结构
Fig. 5 Detailed structure of the ancient Yi character discriminator model
图 6 古彝文字符生成器模型详细结构
Fig. 6 Detailed structure of the ancient Yi character generator model
图 10 通过生成器模型输出图像
$G(\boldsymbol z)$ Fig. 10 Output images
$G(\boldsymbol z)$ from the generator model
图 15 学习率0.2, 0.02, 0.002的损失值变化曲线
Fig. 15 The loss variation of the learning rate involving 0.2, 0.02 and 0.002
图 16 学习率为0.0002, 0.001, 0.002的损失值变化曲线
Fig. 16 The loss variation of the learning rate involving 0.0002, 0.001 and 0.002
图 17 不同训练次数下生成器生成图像
Fig. 17 The generator generates image under different training times
图 20 筛选判别器的训练过程中损失值变化曲线
Fig. 20 The loss curve in process of the training of the selecting discriminator
图 21 训练得到
$\boldsymbol z',$ 然后输入$\boldsymbol z'$ 到生成器得到的图像Fig. 21 After trainning,
$\boldsymbol z'$ is generated, and then input$\boldsymbol z'$ to the generator to get the image
图 24 多形状残缺修复结果
Fig. 24 The repair effect of ancient Yi characterof multiple shape occlusion
图 25 彝文古籍文献中残缺字符修复效果
Fig. 25 The repair effect of incomplete characters in ancient Yi literature
图 26 古彝文残缺字符修复失败效果
Fig. 26 The failed repair effect of ancient Yi incomplete characters
表 1 判别器模型参数表
Table 1 Parameter table of the discriminator model
层信息 卷积核
个数卷积核
大小步长 特征图
大小参数个数 C1层 (卷积层) 128 5×5 2 32×32 3328 C2层 (卷积层) 256 5×5 2 16×16 819456 C3层 (卷积层) 512 5×5 2 8×8 3277312 C4层 (卷积层) 1024 5×5 2 4×4 13108224 OUTPUT层 (输出层) 1×1 16385 
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表 2 生成器模型参数表
Table 2 Parameter table of the generator model
层信息 卷积核
个数卷积核
大小步长 特征图
大小参数个数 C1层 (卷积层) 1654784 C2层 (卷积层) 512 5×5 2 8×8 13107712 C3层 (卷积层) 256 5×5 2 16×16 3277056 C4层 (卷积层) 128 5×5 2 32×32 819328 OUTPUT层 (输出层) 1 5×5 2 64×64 3201
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表 3 古彝文字符修复比例
Table 3 Restoration proportion of ancient Yi characters
个数 占比 (%) 时间消耗 (ms) 完全修复 523 52 52 部分修复 246 25 61 未完成修复 231 23 56 合计 1000 100
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