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Historical palm-leaf manuscript and early paper documents from Indiansubcontinent form an important part of the world's literary and culturalheritage. Despite their importance, large-scale annotated Indic manuscriptimage datasets do not exist. To address this deficiency, we introduceIndiscapes, the first ever dataset with multi-regional layout annotations forhistorical Indic manuscripts. To address the challenge of large diversity inscripts and presence of dense, irregular layout elements (e.g. text lines,pictures, multiple documents per image), we adapt a Fully Convolutional DeepNeural Network architecture for fully automatic, instance-level spatial layoutparsing of manuscript images. We demonstrate the effectiveness of proposedarchitecture on images from the Indiscapes dataset. For annotation flexibilityand keeping the non-technical nature of domain experts in mind, we alsocontribute a custom, web-based GUI annotation tool and a dashboard-styleanalytics portal. Overall, our contributions set the stage for enablingdownstream applications such as OCR and word-spotting in historical Indicmanuscripts at scale.

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