Advertisement

Design of Image Generation System for DCGAN Based Picture Book Text

  • JaeHyeon Cho
  • Nammee MoonEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 536)

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.

Keywords

OCR KoNLPy DCGAN Image generation 

Notes

Acknowledgments

This work has supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2017R1A2B4008886).

References

  1. 1.
    Kim, I.-T., Yoo, K.-J.: Effects of augmented reality picture book on the language expression and flow of young children’s in picture book reading activities. J. Korea Open Assoc. Early Child. Educ. 23(1), 83–109 (2018)CrossRefGoogle Scholar
  2. 2.
    Ryu, K.M., Kim, H.J., Kim, H.J., Lee, E.J.I., Heo, J.Y.: A development of interactive storybook with digital board and smart device. 한국HCI학회 학술대회, pp. 1179–1182 (2017)Google Scholar
  3. 3.
    Kim, Y., Park, H.: Study on the relation between young children’s smart device immersion tendency and their playfulness. Early Child. Educ. Res. Rev. 20(4), 337–353 (2016)Google Scholar
  4. 4.
    Lee, G.-C., Yoo, J.: Development an Android based OCR Application for Hangul Food Menu. J. Korea Inst. Inf. Commun. Eng. 21(5), 951–959 (2017)Google Scholar
  5. 5.
    Smith, R.: An overview of the tesseract OCR engine. In: Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), Parana, pp. 629–633 (2007)Google Scholar
  6. 6.
    Park, E.L., Cho, S.: KoNLPy: Korean natural language processing in Python. In: Proceedings of the 26th Annual Conference on Human & Cognitive Language Technology, pp. 133–136 (2014)Google Scholar
  7. 7.
    Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434 (2015)
  8. 8.
    Han, Y., Kim, H.J.: Face morphing using generative adversarial networks. J. Digital Contents Soc. 19(3), 435–443 (2018)Google Scholar
  9. 9.
    Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., Lee, H.: Generative adversarial text to image synthesis. In: Proceedings of the 33rd International Conference on Machine. Learning, New York, NY, USA, 2016, JMLR: W & CP, vol. 48 (2016)Google Scholar
  10. 10.
    Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for simplicity: the all convolutional net. In: Proceedings of the International Conference on Learning Representations, pp. 1–14, arXiv preprint arXiv:1412.6806 (2015)
  11. 11.
    Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., Lee, H.: Proceedings of The 33rd International Conference on Machine Learning, PMLR, vol. 48, pp. 1060–1069 (2016)Google Scholar
  12. 12.
    Triantafyllidou, D., Tefas, A.: Face detection based on deep convolutional neural networks exploiting incremental facial part learning. In: Proceeding of the International Conference on Pattern Recognition, pp. 3560–3565 (2016)Google Scholar
  13. 13.
    Miller, E.L., Huang, G., RoyChowdhury, A., Li, H., Hua, G.: Labeled faces in the wild: a survey. In: Kawulok, M., Celebi, M., Smolka, B. (eds.) Advances in Face Detection and Facial Image Analysis, pp. 189–248 (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Division of Computer and Information EngineeringHoseo UniversityAsanSouth Korea

Personalised recommendations