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

TL;DR

This paper introduces Indiscapes, a novel dataset with layout annotations for historical Indic manuscripts, and proposes a deep learning architecture for automatic instance-level layout parsing, facilitating OCR and related tasks.

Contribution

The paper presents the first dataset with multi-regional layout annotations for Indic manuscripts and adapts a neural network for automatic layout parsing in complex, dense manuscript images.

Findings

Effective layout parsing on the Indiscapes dataset

Development of a flexible annotation tool for domain experts

Enabling downstream OCR and word-spotting applications

Abstract

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.