Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic Manuscripts
This work addresses the problem of large-scale analysis of historical Indic manuscripts for cultural heritage preservation, though it is incremental as it adapts existing methods to a new domain.
The authors tackled the lack of annotated datasets for historical Indic manuscripts by introducing Indiscapes, the first multi-regional layout annotation dataset, and adapted a Fully Convolutional Deep Neural Network for automatic instance-level layout parsing, demonstrating its effectiveness on this dataset.
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.