Panoptic-DLA: Document Layout Analysis of Historical Newspapers Based on Proposal-Free Panoptic Segmentation Model.

2021 
In this paper, we introduce a novel historical newspaper layout analysis model named Panoptic-DLA. Different from the previous works regarding layout analysis as a separate object detection or semantic segmentation problem, we define the layout analysis task as the proposal-free panoptic segmentation to assign a unique value to each pixel in the document image, encoding both semantic label and instance id. The model consists of two branches: the semantic segmentation branch and the instance segmentation branch. Firstly, the pixels are separated to “things” and “stuff” by semantic classification taking the background as “stuff”, and content objects such as images, paragraphs, etc., as “things”. Then the predicted “things” are grouped further to their instance ids by instance segmentation. The semantic segmentation branch adopted DeepLabV3+ to predict pixel-wise class labels. In order to split adjacent regions well, the instance segmentation branch produce a mountain-like soft score-map and a center-direction map to represent content objects. The method is trained and tested on a dataset of historical European newspapers with complex content layout. The experiment shows that the proposed method achieves the competitive results against popular layout analysis methods. We also demonstrate the effectiveness and superiority of the methods compared to the previous methods.
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