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

arXiv: https://arxiv.org/abs/2108.09436 · HTML: https://mlsanity.com/paper/2108.09436/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 30 references that are in the map.
- First posted on arXiv 21 Aug 2021.

## Builds on (most cited first, 17 of 17)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [LVIS: A Dataset for Large Vocabulary Instance Segmentation](https://mlsanity.com/paper/1908.03195/) (2019)
- [PointRend: Image Segmentation as Rendering](https://mlsanity.com/paper/1912.08193/) (2019)
- [PubLayNet: largest dataset ever for document layout analysis](https://mlsanity.com/paper/1908.07836/) (2019)
- [Conditional Convolutions for Instance Segmentation](https://mlsanity.com/paper/2003.05664/) (2020)
- [DocBank: A Benchmark Dataset for Document Layout Analysis](https://mlsanity.com/paper/2006.01038/) (2020)
- [Boundary-preserving Mask R-CNN](https://mlsanity.com/paper/2007.08921/) (2020)
- [dhSegment: A generic deep-learning approach for document segmentation](https://mlsanity.com/paper/1804.10371/) (2018)
- [CDeC-Net: Composite Deformable Cascade Network for Table Detection in Document Images](https://mlsanity.com/paper/2008.10831/) (2020)
- [Joint Layout Analysis, Character Detection and Recognition for Historical Document Digitization](https://mlsanity.com/paper/2007.06890/) (2020)
- [docExtractor: An off-the-shelf historical document element extraction](https://mlsanity.com/paper/2012.08191/) (2020)
- [Beyond Fixed Grid: Learning Geometric Image Representation with a Deformable Grid](https://mlsanity.com/paper/2008.09269/) (2020)
- [Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers](https://mlsanity.com/paper/2002.06144/) (2020)
- [Labeling, Cutting, Grouping: an Efficient Text Line Segmentation Method for Medieval Manuscripts](https://mlsanity.com/paper/1906.11894/) (2019)
- [Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic Manuscripts](https://mlsanity.com/paper/1912.07025/) (2019)

## Built on by (most cited first, 2 of 2)

- [Text region detection in historical astronomical diagrams](https://mlsanity.com/paper/2606.15886/) (2026)
- [Impact of Iterative Fine-Tuning on Transcription Accuracy in Complex Historical Sanskrit Manuscripts](https://mlsanity.com/paper/2608.18696/) (2026)

## Abstract (arXiv)

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.