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

TL;DR

Palmira is a novel deep deformable network designed for robust instance segmentation of dense, uneven layouts in handwritten manuscripts, especially effective on low-resource Indic palm-leaf documents.

Contribution

The paper introduces Indiscapes2 dataset and Palmira network, advancing segmentation robustness for complex, deformed handwritten manuscript layouts.

Findings

Palmira outperforms baseline methods in segmentation accuracy.

Indiscapes2 dataset is 150% larger than previous datasets.

Palmira generalizes well to various historical manuscript scripts.

Abstract

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