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.
In this paper, we propose a robust and fast line segment detector, which achieves accurate results with a controlled
number of false detections and requires no parameter tuning. It consists of three steps: first, we propose a novel edge
point chaining method to extract Canny edge segments (i.e., contiguous chains of Canny edge points) from the input
image; second, we propose a top-down scheme based on smaller eigenvalue analysis to extract line segments within each
obtained edge segment; third, we employ Desolneux et al.’s method to reject false detections. Experiments demonstrate
that it is very efficient and more robust than two state of the art methods—LSD and EDLines.