Abstract
The popularity of comics has increased in the digital era, leading to the development of several applications and platforms. These advancements have opened up new opportunities for creating and distributing comics and experimenting with new forms of visual storytelling. One of the most promising research areas in this field is the use of deep learning techniques to process comic book images. However, one of the main challenges associated with the use of these models is adapting them to different domains because comics greatly vary in style, subject matter, and design. In this paper, we present a study on the problem of generalization across different domains for the automatic detection of characters in comics. We evaluate the performance of state-of-the-art models trained in different domains and analyze the difficulties and challenges associated with generalization. Our study provides insights into the development of more robust deep-learning models for processing comics’ characters and improving their generalization to new domains.
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References
Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Lechevallier, Y., Saporta, G. (eds.) Proceedings of COMPSTAT’2010, pp. 177–186. Springer-Verlag, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-7908-2604-3_16
Dutta, A., Biswas, S.: CNN based extraction of panels/characters from Bengali comic book page images. In: 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), vol. 1, pp. 38–43. IEEE (2019)
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)
Fujimoto, A., Ogawa, T., Yamamoto, K., Matsui, Y., Yamasaki, T., Aizawa, K.: Manga109 dataset and creation of metadata. In: Proceedings of the 1st International Workshop on Comics Analysis, Processing and Understanding, pp. 1–5 (2016)
Girshick, R.: Fast r-cnn. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1440–1448 (2015). https://doi.org/10.1109/ICCV.2015.169
Guérin, C., et al.: eBDtheque: a representative database of comics. In: 2013 12th International Conference on Document Analysis and Recognition, pp. 1145–1149. IEEE (2013)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Iyyer, M., et al.: The amazing mysteries of the gutter: drawing inferences between panels in comic book narratives. In: Proceedings of the IEEE Conference on Computer Vision and Pattern recognition, pp. 7186–7195 (2017)
Jocher, G.: YOLOv5 by Ultralytics, May 2020. https://doi.org/10.5281/zenodo.3908559, https://github.com/ultralytics/yolov5
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Lladós, J.: Two decades of GREC workshop series. Conclusions of GREC2017. In: Fornés, A., Lamiroy, B. (eds.) GREC 2017. LNCS, vol. 11009, pp. 163–168. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02284-6_14
Nguyen, N.V., Rigaud, C., Burie, J.C.: Comic characters detection using deep learning. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 3, pp. 41–46. IEEE (2017)
Nguyen, N.V., Rigaud, C., Burie, J.C.: Digital comics image indexing based on deep learning. J. Imaging 4(7), 89 (2018)
Padilla, R., Passos, W.L., Dias, T.L., Netto, S.L., Da Silva, E.A.: A comparative analysis of object detection metrics with a companion open-source toolkit. Electronics 10(3), 279 (2021)
Rayar, F.: Accessible comics for visually impaired people: challenges and opportunities. In: 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 3, pp. 9–14. IEEE (2017)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)
Xiao, Y., et al.: A review of object detection based on deep learning. Multimed. Tools Appl. 23729–23791 (2020). https://doi.org/10.1007/s11042-020-08976-6
Zhao, Z.Q., Zheng, P., Xu, S.t., Wu, X.: Object detection with deep learning: a review. IEEE Trans. Neural Netw. Learn. Syst. 30(11), 3212–3232 (2019)
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Lucas, J., Gallego, A.J., Calvo-Zaragoza, J., Martinez-Sevilla, J.C. (2023). Automatic Detection of Comic Characters: An Analysis of Model Robustness Across Domains. In: Coustaty, M., Fornés, A. (eds) Document Analysis and Recognition – ICDAR 2023 Workshops. ICDAR 2023. Lecture Notes in Computer Science, vol 14193. Springer, Cham. https://doi.org/10.1007/978-3-031-41498-5_11
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DOI: https://doi.org/10.1007/978-3-031-41498-5_11
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