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Handwritten documents are often characterized by dense and uneven layout.Despite advances, standard deep network based approaches for semantic layoutsegmentation are not robust to complex deformations seen across semanticregions. This phenomenon is especially pronounced for the low-resource Indicpalm-leaf manuscript domain. To address the issue, we first introduceIndiscapes2, a new large-scale diverse dataset of Indic manuscripts withsemantic layout annotations. Indiscapes2 contains documents from four differenthistorical collections and is 150% larger than its predecessor, Indiscapes. Wealso propose a novel deep network Palmira for robust, deformation-awareinstance segmentation of regions in handwritten manuscripts. We also reportHausdorff distance and its variants as a boundary-aware performance measure.Our experiments demonstrate that Palmira provides robust layouts, outperformsstrong baseline approaches and ablative variants. We also include qualitativeresults on Arabic, South-East Asian and Hebrew historical manuscripts toshowcase the generalization capability of Palmira.

Palmira: A Deep Deformable Network for Instance Segmentation of Dense and Uneven Layouts in Handwritten Manuscripts