An End-to-End Quadrilateral Regression Network for Comic Panel Extraction.

Comic panel extraction, i.e., decomposing a comic page image into panels, has become a fundamental technique for meeting many practical needs of mobile comic reading such as comic content adaptation and comic animating. Most of existing approaches are based on handcrafted low-level visual patterns and heuristics rules, thus having limited ability to deal with irregular comic panels. Only one existing method is based on deep learning and achieves better experimental results, but its architecture is redundant and its time efficiency is not good. To address these problems, we propose an end-to-end, two-stage quadrilateral regressing network architecture for comic panel detection, which inherits the architecture of Faster R-CNN. At the first stage, we propose a quadrilateral region proposal network for generating panel proposals, based on a newly proposed quadrilateral regression method. At the second stage, we classify the proposals and refine their shapes with the proposed quadrilateral regression method again. Extensive experimental results demonstrate that the proposed method significantly outperforms the existing comic panel detection methods on multiple datasets by F1-score and page accuracy.