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


Jaehyeon Cho, Nammee Moon, Journal of Information Processing Systems Vol. 16, No. 6, pp. 1437-1446, Dec. 2020  

10.3745/JIPS.02.0149
Keywords: DCGAN, NLTK, OCR
Fulltext:

Abstract

For the last few years, smart devices have begun to occupy an essential place in the life of children, by allowing them to access a variety of language activities and books. Various studies are being conducted on using smart devices for education. Our study extracts images and texts from kids’ book with smart devices and matches the extracted images and texts to create new images that are not represented in these books. The proposed system will enable the use of smart devices as educational media for children. A deep convolutional generative adversarial network (DCGAN) is used for generating a new image. Three steps are involved in training DCGAN. Firstly, images with 11 titles and 1,164 images on ImageNet are learned. Secondly, Tesseract, an optical character recognition engine, is used to extract images and text from kids’ book and classify the text using a morpheme analyzer. Thirdly, the classified word class is matched with the latent vector of the image. The learned DCGAN creates an image associated with the text.


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Cite this article
[APA Style]
Cho, J. & Moon, N. (2020). Design of Image Generation System for DCGAN-Based Kids’ Book Text. Journal of Information Processing Systems, 16(6), 1437-1446. DOI: 10.3745/JIPS.02.0149.

[IEEE Style]
J. Cho and N. Moon, "Design of Image Generation System for DCGAN-Based Kids’ Book Text," Journal of Information Processing Systems, vol. 16, no. 6, pp. 1437-1446, 2020. DOI: 10.3745/JIPS.02.0149.

[ACM Style]
Jaehyeon Cho and Nammee Moon. 2020. Design of Image Generation System for DCGAN-Based Kids’ Book Text. Journal of Information Processing Systems, 16, 6, (2020), 1437-1446. DOI: 10.3745/JIPS.02.0149.