Comic page image understanding aims to analyse the layout of the comic page images by detecting the storyboards and identifying the reading order automatically. It is the key technique to produce the digital comic documents suitable for reading on mobile devices. In this paper, we propose a novel comic page image understanding method based on edge segment analysis. First, we propose an efficient edge point chaining method to extract Canny edge segments (i.e., contiguous chains of Canny edge points) from the input comic page image; second, we propose a top-down scheme to detect line segments within each obtained edge segment; third, we develop a novel method to detect the storyboards by selecting the border lines and further identify the reading order of these storyboards. The proposed method is performed on a data set consisting of 2000 comic page images from ten printed comic series. The experimental results demonstrate that the proposed method achieves satisfactory results on different comics and outperforms the existing methods.
Proc. SPIE. 8658, Document Recognition and Retrieval XX
KEYWORDS: Image processing algorithms and systems, Mobile devices, Lithium, Detection and tracking algorithms, Image segmentation, Image processing, Digital imaging, Detector development, Image understanding, Algorithm development
Comic image understanding aims to automatically decompose scanned comic page images into storyboards and then
identify the reading order of them, which is the key technique to produce digital comic documents that are suitable for
reading on mobile devices. In this paper, we propose a novel comic image understanding method based on polygon
detection. First, we segment a comic page images into storyboards by finding the polygonal enclosing box of each
storyboard. Then, each storyboard can be represented by a polygon, and the reading order of them is determined by
analyzing the relative geometric relationship between each pair of polygons. The proposed method is tested on 2000
comic images from ten printed comic series, and the experimental results demonstrate that it works well on different
types of comic images.