Design of Image Generation System for DCGAN-Based Kids' Book Text

Jaehyeong Cho, Nammee Moon
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引用次数: 4

Abstract

When a picture book is photographed with a smart device, the text is analyzed for meaning and associated images are created. Image creation is the first step in learning DCGAN using class lists and images. In this study, DCGAN was trained with 11 classes and images of 1688 bears, which were collected by ImageNet for design. The second step is to shoot the image and text of the picture book on a smart device, and convert the text part of the shot image into a system readable character. We use the morpheme analyzer to classify nouns and verbs in text, and Discriminator learn to recognize the classified parts of speech as latent vectors of images. The third step is to create an associated image in the text. In the picture book, take the text of the part without the image and extract nouns and verbs. The extracted parts of speech and the learned latent vector are used as Generator parameters to generate images associated with the text.
基于dcgan的儿童图书文本图像生成系统设计
当用智能设备拍摄图画书时,会分析文本的含义并创建相关图像。图像创建是使用类列表和图像学习DCGAN的第一步。在本研究中,DCGAN使用11个类和1688只熊的图像进行训练,这些熊的图像是由ImageNet收集用于设计的。第二步是在智能设备上拍摄绘本的图像和文本,并将拍摄图像的文本部分转换为系统可读字符。我们使用词素分析器对文本中的名词和动词进行分类,鉴别器学习识别分类后的词性作为图像的潜在向量。第三步是在文本中创建一个相关的图像。在绘本中,选取没有图片的部分的文字,提取名词和动词。将提取的语音部分和学习到的潜在向量作为生成器参数,生成与文本相关的图像。
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