CNN Based Extraction of Panels/Characters from Bengali Comic Book Page Images

Abstract

Peoples nowadays prefer to use digital gadgets like cameras or mobile phones for capturing documents. Automatic extraction of panels/characters from the images of a comic document is challenging due to the wide variety of drawing styles adopted by writers, beneficial for readers to read them on mobile devices at any time and useful for automatic digitization. Most of the methods for localization of panel/character rely on the connected component analysis or page background mask and are applicable only for a limited comic dataset. This work proposes a panel/character localization architecture based on the features of YOLO and CNN for extraction of both panels and characters from comic book images. The method achieved remarkable results on Bengali Comic Book Image dataset (BCBId) consisting of total 4130 images, developed by us as well as on a variety of publicly available comic datasets in other languages, i.e. eBDtheque, Manga 109 and DCM dataset.

Quantitative Analysis of Visual Representation of Sign Elements in COVID-19 Context

“…This means that the probabilities of the objects found in the image are simultaneously predicted and adjusted. For YOLO to operate, there is a training stage that the developers themselves have provided for, though it can be trained for a specific set of images ( Burić, Pobar, & Ivašić-Kos, 2019 ; Dutta & Biswas, 2019 ; Ju, Wang, & Chang, 2019 ). YOLOv4 is currently the most accurate and fastest network among current identification tools ( Bochkovskiy, Wang, & Liao, 2020 ; Sumit, Watada, Roy, & Rambli, 2020 ) and was used as the key object identification tool in this research.…”

“…́, Pobar, & Ivašic ́-Kos, 2019;Dutta & Biswas, 2019;Ju, Wang, & Chang, 2019). YOLOv4 is currently the most accurate and fastest network among current identification tools(Bochkovskiy, Wang, & Liao, 2020;Sumit, Watada, Roy, & Rambli, 2020) and was used as the key object identification tool in this research.…”