Comics Datasets Framework: Mix of Comics Datasets for Detection Benchmarking
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-70645-5_11
Reference29 articles.
1. Agrawal, H., Mishra, A., Gupta, M., Mausam: multimodal persona based generation of comic dialogs. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 14150–14164. Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.acl-long.791, https://aclanthology.org/2023.acl-long.791
2. Baek, J., Matsui, Y., Aizawa, K.: COO/ Comic Onomatopoeia Dataset for Recognizing Arbitrary or Truncated Texts. arXiv. https://doi.org/10.48550/arXiv.2207.04675, http://arxiv.org/abs/2207.04675
3. Dubray, D., Laubrock, J.: Deep CNN-based Speech Balloon Detection and Segmentation for Comic Books. arXiv, http://arxiv.org/abs/1902.08137
4. Dunst, A., Hartel, R., Laubrock, J.: The graphic narrative corpus (GNC): design, annotation, and analysis for the digital humanities. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). vol. 03, pp. 15–20. https://doi.org/10.1109/ICDAR.2017.286
5. Dutta, A., Biswas, S., Das, A.K.: BCBId: First Bangla comic dataset and its applications 25(4), 265–279. https://doi.org/10.1007/s10032-022-00412-9
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