# Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic Manuscripts

arXiv: https://arxiv.org/abs/1912.07025 · HTML: https://mlsanity.com/paper/1912.07025/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2019.

## Built on by (most cited first, 7 of 7)

- [Digital Peter: Dataset, Competition and Handwriting Recognition Methods](https://mlsanity.com/paper/2103.09354/) (2021)
- [Palmira: A Deep Deformable Network for Instance Segmentation of Dense and Uneven Layouts in Handwritten Manuscripts](https://mlsanity.com/paper/2108.09436/) (2021)
- [Evaluation of a Region Proposal Architecture for Multi-task Document Layout Analysis](https://mlsanity.com/paper/2106.11797/) (2021)
- [Multi-Expert Routing for Multi-Domain Low-Resource OCR: A Manchu Case Study](https://mlsanity.com/paper/2607.14041/) (2026)
- [BoundaryNet: An Attentive Deep Network with Fast Marching Distance Maps for Semi-automatic Layout Annotation](https://mlsanity.com/paper/2108.09433/) (2021)
- [Accurate Fine-grained Layout Analysis for the Historical Tibetan Document Based on the Instance Segmentation](https://mlsanity.com/paper/2110.08164/) (2021)
- [UniLipi: A Unified Multi-Script OCR for Historical Indic Manuscripts](https://mlsanity.com/paper/2608.28195/) (2026)

## Abstract (arXiv)

Historical palm-leaf manuscript and early paper documents from Indian subcontinent form an important part of the world's literary and cultural heritage. Despite their importance, large-scale annotated Indic manuscript image datasets do not exist. To address this deficiency, we introduce Indiscapes, the first ever dataset with multi-regional layout annotations for historical Indic manuscripts. To address the challenge of large diversity in scripts and presence of dense, irregular layout elements (e.g. text lines, pictures, multiple documents per image), we adapt a Fully Convolutional Deep Neural Network architecture for fully automatic, instance-level spatial layout parsing of manuscript images. We demonstrate the effectiveness of proposed architecture on images from the Indiscapes dataset. For annotation flexibility and keeping the non-technical nature of domain experts in mind, we also contribute a custom, web-based GUI annotation tool and a dashboard-style analytics portal. Overall, our contributions set the stage for enabling downstream applications such as OCR and word-spotting in historical Indic manuscripts at scale.