2 research outputs found
Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic Manuscripts
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.Comment: Oral presentation at International Conference on Document Analysis
and Recognition (ICDAR) - 2019. For dataset, pre-trained networks and
additional details, visit project page at http://ihdia.iiit.ac.in