Have a personal or library account? Click to login
Automatic Detection of Four-Panel Cartoon in Large-Scale Korean Digitized Newspapers using Deep Learning Cover

Automatic Detection of Four-Panel Cartoon in Large-Scale Korean Digitized Newspapers using Deep Learning

Open Access
|Jun 2024

References

  1. 1Arie, L. G. (2022). The practical guide for object detection with YOLOv5 algorithm. Medium. Retrieved from: https://towardsdatascience.com/the-practical-guide-for-object-detection-with-yolov5-algorithm-74c04aac4843 (last accessed: 16 April 2024).
  2. 2Cao, Z., Liao, T., Song, W., Chen, Z., & Li, C. (2020). Detecting the shuttlecock for a badminton robot: A YOLO based approach. Expert Systems with Applications, 164(8), 113833. DOI: 10.1016/j.eswa.2020.113833
  3. 3Chen, X., Xiang, S., Liu, C.-L., & Pan, C.-H. (2013). Vehicle detection in satellite images by parallel deep convolutional neural networks. 2013 2nd IAPR Asian Conference on Pattern Recognition. Naha, Japan. DOI: 10.1109/ACPR.2013.33
  4. 4ChosunIlbo. (2024). Chosun Ilbo. Retrieved from: https://100image.chosun.com/cs-digitizing-images/1933/06/19330622/Image/news-cs-4325-19330622-e-1-03-300-b1-bw-thumb-1100.jpg. (last accessed: 16 April 2024).
  5. 5ChosunIlboNewsLibrary. (2024). ChosunIlboNewsLibrary. Retrieved from: https://newslibrary.chosun.com (last accessed: 16 April 2024).
  6. 6Chung, H. (2016). Images of modern city, Gyeongseong seen from Manwha Fool series. History Forum, 43(12). DOI: 10.14380/AHF.2016.43.179
  7. 7Darkpgmr. (2021). Darklabel. GitHub. Retrieved from: https://github.com/darkpgmr/DarkLabel (last accessed: 2 March 2023).
  8. 8DongaIlbo. (2024). DongaIlbo. Retrieved from: https://www.donga.com (last accessed: 17 April 2024).
  9. 9Dwyer, B., Nelson, J., & Solawetz, J. (2022). Roboflow (version 1.0) [software]. Retrieved from: https://roboflow.com (last accessed: 13 January 2023).
  10. 10Fiala, M. L., Nowitzki, O., Engelhardt-Nowitzki, C., & Wöber, W. (2022). Optical music recognition of printed white mensural notation: Conversion to modern notation using object detection mechanisms. International Journal of Humanities and Arts Computing, 16(1), 3349. DOI: 10.3366/ijhac.2022.0275
  11. 11Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2013). Rich feature hierarchies for accurate object detection and semantic segmentation. arXiv. Retrieved from: https://arxiv.org/abs/1311.2524. DOI: 10.1109/CVPR.2014.81
  12. 12Hodel, T., Schoch, D., Schneider, C., & Purcell, J. (2021). General models for handwritten text recognition: Feasibility and state-of-the art. German Kurrent as an example. Journal of Open Humanities Data, 7(13), 13. DOI: 10.5334/johd.46
  13. 13Hoi, S. C. H., Wu, X., Liu, H., Wu, Y., Wang, H., Xue, H., & Wu, Q. (2015). LOGO-Net: Largescale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks. arXiv, abs/1511.02462. Retrieved from: https://arxiv.org/abs/1511.02462.
  14. 14Jocher, G. (2020). YOLOv5 by Ultralytics. GitHub. Retrieved from: https://github.com/ultralytics/yolov5 (Version 7.0). (last accessed: 5 February 2023).
  15. 15Jones, E. A., & Aoki, C. (1988). The Processing of Japanese Kana and Kanji Characters. In D. de Kerckhove & C. J. Lumsden (Eds.), The Alphabet and the Brain. DOI: 10.1007/978-3-662-01093-8
  16. 16JoongAngIlbo. (2024). JoongAngIlbo. Retrieved from: https://www.joongang.co.kr (last accessed: 17 April 2024).
  17. 17Kim, K. (2005). Reading newspapers all at once during the August 15th Liberation. ChosunIlbo. Retrieved from: https://www.chosun.com/site/data/htmldir/2005/11/21/2005112170320.html (last accessed: 13 November 2023).
  18. 18Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Imagenet classification with Deep Convolutional Neural Networks. Commun. ACM, 60(6), 8490. DOI: 10.1145/3065386
  19. 19Lee, S., Kim, B., & Jun, B. (2023a). YOLOv5_FPC-Detector Script utilizing the Google Colab platform. https://colab.research.google.com/drive/1qnCKaUGUTF5vSRdPc7DI6y7b05P8yuQ?usp=sharing (last accessed: 9 December 2023).
  20. 20Lee, S., Kim, B., & Jun, B. (2023b). YOLOv5_FPC Train, valid, test.zip. https://drive.google.com/file/d/1F5iJ5bx-EKTlj06X5zA0yxLo1sVZBV2l/view?usp=sharing (last accessed: 19 October 2023).
  21. 21Lee, S., Kim, B., & Jun, B. (2024a). Excel File of Metadata for YOLOv5_FPC Detected Images. Harvard Dataverse. DOI: 10.7910/DVN/DFVZWE.
  22. 22Lee, S., Kim, B., & Jun, B. (2024b). YOLOv5_FPC-Detector script. GitHub. Retrieved from: https://github.com/researchgitrepository/FPC_Research/blob/main/YOLOv5_FPC-Detector%20script (last accessed: 16 April 2024).
  23. 23Lee, W.-S. (2005). A Study on the Rhetoric Expression in Domestic and Foreign 4 Panel Comic Strips. Cartoon and Animation Studies, Serial No. 9, 1832. Retrieved from: https://koreascience.kr/article/JAKO200522941432318.page
  24. 24Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A., van Ginneken, B., & Sanchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 6088. DOI: 10.1016/j.media.2017.07.005
  25. 25Park, K.-C. (2010). Research on the Structure of 4 Panel Comic Strips and Rhetoric Expression for the Education of Comic Contents: With of Kyunghyangsinmoon. Cartoon and Animation Studies, 19, 1735. Retrieved from: https://koreascience.kr/article/JAKO201029848353457.page
  26. 26Rajeshwari, P., Abhishek, P., Srikanth, P., & Vinod, T. (2019). Object detection: An Overview. International Journal of Trend in Scientific Research and Development (IJTSRD), 3(3), 16631665. DOI: 10.31142/ijtsrd23422
  27. 27Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, real-time object detection. University of Washington, Allen Institute for AI, Facebook AI Research. DOI: 10.1109/CVPR.2016.91
  28. 28Sa, E.-S. (2009). Development of Press Freedom in South Korea Since Japanese Colonial Rule. Asian Culture and History, 1(2), 317. DOI: 10.5539/ach.v1n2p3
  29. 29Siddiqui, N. (2024). Cutting the Frame: An In-Depth Look at the Hitchcock Computer Vision Dataset. Journal of Open Humanities Data, 10(5), 5. DOI: 10.5334/johd.163
  30. 30Smirnov, S., & Eguizabal, A. (2018). Deep learning for object detection in fine-art paintings. 2018 Metrology for Archaeology and Cultural Heritage (MetroArchaeo). Cassino, Italy. DOI: 10.1109/MetroArchaeo43810.2018.9089828
  31. 31Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014). Deepface: Closing the gap to human-level performance in face verification. 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA. DOI: 10.1109/CVPR.2014.220
  32. 32Xiao, Y., Tian, Z., Yu, J., Zhang, W., & Sun, K. (2020). A review of object detection based on deep learning. Multimedia Tools and Applications, 79(35), 2372923791. DOI: 10.1007/s11042-020-08976-6
DOI: https://doi.org/10.5334/johd.205 | Journal eISSN: 2059-481X
Language: English
Submitted on: Mar 6, 2024
Accepted on: Apr 19, 2024
Published on: Jun 6, 2024
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2024 Seojoon Lee, Byungjun Kim, Bong Gwan Jun, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.