In this work, we propose BoundaryNet, a novel resizing-free approach for high-precision semi-automatic layout annotation. The variable-sized user selected region of interest is first processed by an attention-guided skip network. The network optimization is guided via Fast Marching distance maps to obtain a good quality initial boundary estimate and an associated feature representation. These outputs are processed by a Residual Graph Convolution Network optimized using Hausdorff loss to obtain the final region boundary. Results on a challenging image manuscript dataset demonstrate that BoundaryNet outperforms strong baselines and produces high-quality semantic region boundaries. Qualitatively, our approach generalizes across multiple document image datasets containing different script systems and layouts, all without additional fine-tuning.

The architecture of BoundaryNet (top) and various sub-components (bottom). The variable-sized H×W input image is processed by Mask-CNN (MCNN) which predicts a region mask estimate and an associated region class. The mask’s boundary is determined using a contourization procedure (light brown) applied on the estimate from MCNN. M boundary points are sampled on the boundary. A graph is constructed with the points as nodes and edge connectivity defined by 6 k-hop neighborhoods of each point. The spatial coordinates of a boundary point location p = (x, y) and corresponding back- bone skip attention features from MCNN f^r are used as node features for the boundary point. The feature-augmented contour graph G = (F, A) is iteratively processed by Anchor GCN to obtain the final output contour points defining the region boundary.

Paper

Code [PyTorch]

Supplementary