International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

AI-Generated Comic Strips

Perera M.P.M.Faculty of Computing, Sri Lanka Institute of Information Technology, New Kandy RD, Malabe, Sri LankaKollure K.A.D.D.Faculty of Computing, Sri Lanka Institute of Information Technology, New Kandy RD, Malabe, Sri LankaGunasekara A.M.P.P.Faculty of Computing, Sri Lanka Institute of Information Technology, New Kandy RD, Malabe, Sri LankaC.D. AdhihettyFaculty of Computing, Sri Lanka Institute of Information Technology, New Kandy RD, Malabe, Sri LankaDr. Nuwan KodagodaFaculty of Computing, Sri Lanka Institute of Information Technology, New Kandy RD, Malabe, Sri LankaAmitha CalderaFaculty of Computing, Sri Lanka Institute of Information Technology, New Kandy RD, Malabe, Sri Lanka

Vol 7 No 9 (2023): Volume 7, Issue 9, September 2023 | Pages: 74-82

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 28-09-2023

doi Logo doi.org/10.47001/IRJIET/2023.709008

pdf iconFull Text PDF
Abstract

Comic strips have long been a popular method of storytelling, capturing the imagination of readers of all ages and evolving into a wide range of genres today, from superhero comics to political comics. Studies show that comic strips have a significant impact on readers’ literacy, empathy, and openness to social issues. Comics have the ability to alter how to perceive the world. This research paper presents a system (ComicGenie) for automatically creating comic strips for Batman based on user-entered text descriptions. The proposed system consists of distinct components such as character detection, environment detection, text bubble generation, and voiceover over the scenario, each specializing in different aspects of comic strip creation. SVM and fastText technologies in NLP were utilized for text classification when developing models. Overall, this research contributes to the field of comic strip creation, offering a comprehensive web application.  The ComicGenie holds significant potential to revolutionize the comic industry and inspire new avenues for computer-generated storytelling.

Keywords

Comic strips, Batman, NLP, Text classification, FastText, SVM, ComicGenie


Citation of this Article

Perera M.P.M., Kollure K.A.D.D., Gunasekara A.M.P.P., C.D. Adhihetty, Dr. Nuwan Kodagoda, Amitha Caldera, “AI-Generated Comic Strips” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 7, Issue 9, pp 74-82, September-2023. Article DOI https://doi.org/10.47001/IRJIET/2023.709008

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
References
  1. Text block segmentation in comic speech bubbles Christophe Rigaud, Nhu-Van Nguyen, Jean Christophe Burie
  2. Reddy, A., & Balaji. (2019). Incorporating Visual and Textual Cues in Dialogue Generation: An Application to Comic Strips, 9.
  3. Liu, X., Wang, Y., Tang, Z., 2015. A clump splitting based method to localize speech balloons in comics, in: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), IEEE. pp. 901–905.
  4. Yamada, M., Budiarto, R., Endo, M., Miyazaki, S., 2004. Comic image decomposition for reading comics on cellular phones. IEICE transactions on information and systems.
  5. Understanding Art with AI: Our Research Experience by G Castellano · 2021 — Understanding Art with AI: Our Research Experience. Giovanna Castellano, Gennaro Vessio
  6. Wang, Y., Zhou, Y., Liu, D., Tang, Z., 2016. Comic storyboard extraction via edge segment analysis. Multimedia Tools and Applications
  7. Tanaka, T., Shoji, K., Toyama, F., Miyamichi, J., 2007. Layout analysis of treestructured scene frames in comic images, in: IJCAI.
  8. Su, C.Y., Chang, R.I., Liu, J.C., 2011. Recognizing text elements for svg comic compression and its novel applications, in: 2011 International Conference on Document Analysis and Recognition, IEEE. pp. 1329–1333
  9. Ponsard, C., Ramdoyal, R., Dziamski, D., 2012. An ocr-enabled digital comic books viewer, in: International Conference on Computers for Handicapped Persons, Springer. pp. 471–478
  10. Burie, J.C., Ogier, J.M., Ho, A.K.N., 2011. Comics page structure analysis based on automatic panel extraction.
  11. Wang, Z., Romat, H., Chevalier, F., & Bach, B. (2021). Interactive Data Comics. ResearchGate. https://www.researchgate.net/publication/353825597_Interactive_Data_Comics
  12. Liang Qiu, Yizhou Zhao, Jinchao Li, Pan Lu , Baolin Peng , Jianfeng Gao, Song-Chun Zhu, 2022 Association for the Advancement of Artificial Intelligence (www.aaai.org)
  13. Shibly, K. H., Rahman, S., Dey, S. K., & Shamim, S. H. (2020). Advanced artistic style transfer using deep neural network. In Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-52856-0_49
  14. Author links open overlay panelChongming Gao a et al. (2021) Advances and challenges in conversational recommender systems: A survey, AI Open. Available at: https://www.sciencedirect.com/science/article/pii/S2666651021000164 (Accessed: 14 August 2023).
  15. Ingold, T. (2005). The eye of the storm: visual perception and the weather. Visual Studies, 20(2), 97–104. https://doi.org/10.1080/14725860500243953
  16. A. Kusnadi and D. Julio, "Security system with 3 dimensional face recognition using PCA method and neural networks algorithm", 2017 4th International Conference on New Media Studies (CONMEDIA), pp. 152-155, November 2017.
  17. Ahmad, A., Amin, M. R.: Bengali word embeddings and its application in solving document classification problem. In: 2016 19th International Conference on Computer and Information Technology (ICCIT), pp. 425–430. IEEE (2016)
  18. L. A. Gatys, A. S. Ecker and M. Bethge, A neural algorithm of artistic style, 2015.
  19. D. Rahmawati and M. L. Khodra, "Word2vec semantic representation in multilabel classification for Indonesian news article", 2016 International Conference On Advanced Informatics: Concepts Theory And Application (ICAICTA), 2016.
  20. Chiu, J.P., Nichols, E.: Named entity recognition with bidirectional LSTM-CNNs. Trans. Assoc. Comput. Linguist. 4, 357–370 (2016)
  21. Figueiredo, F., Rocha, L., Couto, T., Salles, T., Gonçalves, M.A., Meira Jr., W.: Word co-occurrence features for text classification. Inf. Syst. 36(5), 843–858 (2011)
  22. Zhang, D., Xu, H., Su, Z., Xu, Y.: Chinese comments sentiment classification based on Word2vec and SVM perf. Expert Syst. Appl. 42(4), 1857–1863 (2015)
  23. Goldberg, Y.: Neural network methods for natural language processing. Synth. Lect. Hum. Lang. Technol. 10(1), 117–118 (2017)
  24. Sumit, S.H., Hossan, M.Z., Al Muntasir, T., Sourov, T.: Exploring word embedding for Bangla sentiment analysis. In: 2018 International Conference on Bangla Speech and Language Processing (ICBSLP), pp. 1–5. IEEE (2018)
  25. Řehůřek, R., Sojka, P.: Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, pp. 45–50. ELRA, Valletta, May 2010. http://is.muni.cz/publication/884893/en

Copyright @ 2026 IRJIET. All Right Reserved.

Licensed underCreative Commons Attribution 4.0 International License.