  Precise boundary annotations of image regions can be crucial for downstream
applications which rely on region-class semantics. Some document collections
contain densely laid out, highly irregular and overlapping multi-class region
instances with large range in aspect ratio. Fully automatic boundary estimation
approaches tend to be data intensive, cannot handle variable-sized images and
produce sub-optimal results for aforementioned images. To address these issues,
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. We
integrate BoundaryNet into a document annotation system and show that it
provides high annotation throughput compared to manual and fully automatic
alternatives.
