Deteksi Balon Ucapan Pada Komik Jepang Dengan Convolutional Neural Network, Canny Edge Detection dan Run Length Smooth Algorithm

Ricky Setiawan Saswono, Rudi Adipranata, Kartika Gunadi

Abstract


Comic is an entertainment media that is usually used to fill free time. Comics themselves are already very well known in the world, especially comics from Japan. Comics from Japan, commonly called Manga, have a high level of popularity. The proof is a lot of Manga that is translated into each country's language. Examples such as One Piece that has been circulating in 43 countries. Even so the translation process is quite long especially in Japanese translation.

This research can be used to accelerate the translation process by using CNN and Canny Edge Detection to detect speech balloons in Manga. The detection results are segmented and with the help of OCR to digitize Japanese characters. Then use copy-paste techniques in an online dictionary or online translator to find the meaning of letters that are not understood. Because searching for letters from a physical dictionary (book) takes more time.

The results of the research to segment the speech balloon from Manga were successful but to classify the image in the form of a speech balloon or not with CNN was unsuccessful. Researchers assume because the dataset created is small in number or a problem during pre-processing.

Keywords


CNN; Manga; RLSA; Canny Edge Detection; Speech bubble

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References


Dubray, D., & Laubrock, J. 2019. Deep CNN-based Speech

Balloon Detection and Segmentation for Comic Books. 2019

International Conference on Document Analysis and

Recognition (ICDAR). Sydney, Australia. 1237-1243.

https://doi.org/10.1109/ICDAR.2019.00200

Fang, W., Ding, Y., Zhang, F., & Sheng, V. 2019. DOG: A

new background removal for object recognition from images.

Neurocomputing, 361, 85-91.

https://doi.org/10.1016/j.neucom.2019.05.095

Kuboi, T. 2014. Element Detection in Japanese Comic Book

Panels. Thesis, California Polytechnic State University,

Computer Science, San Luis Obispo.

doi:10.15368/theses.2014.141

Liliana, Budhi, G. S., & Hendra. 2010. Segmentasi Plat

Nomor Kendaraan Dengan Menggunakan Metode RunLength Smearing Algorithm (RLSA). Retrieved from:

https://www.researchgate.net/publication/277124943_Segme

ntasi_Plat_Nomor_Kendaraan_Dengan_Menggunakan_Meto

de_Run-Length_Smearing_Algorithm_RLSA

Ogawa, T., Otsubo, A., Narita, R., Yusuke, M., Yamasaki,

T., & Aizawa , K. 2018. Object Detection for Comics using

Manga109 Annotations. arXiv:1803.08670v2. Retrieved

from https://arxiv.org/abs/1803.08670

Rigaud, C., Burie, J.-C., & Ogier, J.-M. 2017. TextIndependent Speech Balloon Segmentation for Comics and

Manga. International Workshop on Graphics Recognition,

-147. https://doi.org/10.1007/978-3-319-52159-6_10

Simonyan, K., & Zisserman, A. 2015. Very Deep

Convolutional Networks for Large-scale Image Recognition.

arXiv:1409.1556v6. Retrieved from

https://arxiv.org/abs/1409.1556

C. Szegedy et al., Going deeper with convolutions, 2015

IEEE Conference on Computer Vision and Pattern

Recognition (CVPR), 2015, Boston, MA. 1-9,

https://doi.org/10.1109/CVPR.2015.7298594


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