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.

Introduction: The collection and analysis of historical document images is a key component in the preservation of culture and heritage. Given its importance, a number of active research efforts exist across the world BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5. In this paper, we focus on palm-leaf and early paper documents from the Indian sub-continent. In contrast with modern or recent era documents, such manuscripts are considerably more fragile, prone to degradation from elements of nature and tend to have a short shelf life BIBREF6, BIBREF7, BIBREF8. More worryingly, the domain experts who can decipher such content are small in number and dwindling. Therefore, it is essential to access the content within these documents before it is lost forever. Surprisingly, no large-scale annotated Indic manuscript image datasets exist for the benefit of researchers in the community. In this paper, we take a significant step to address this gap by creating such a dataset. Given the large diversity in language, script and non-textual regional elements in these manuscripts, spatial layout parsing is crucial in enabling downstream applications such as OCR, word-spotting, style-and-content based retrieval and clustering. For this reason, we first tackle the problem of creating a diverse, annotated spatial layout dataset. This has the immediate advantage of bypassing the hurdle of language and script familiarity for annotators since layout annotation does not require any special expertise unlike text annotation. In general, manuscripts from Indian subcontinent pose many unique challenges (Figure FIGREF1). To begin with, the documents exhibit a large multiplicity of languages. This is further magnified by variations in intra-language script systems. Along with text, manuscripts may contain pictures, tables, non-pictorial decorative elements in non-standard layouts. A unique aspect of Indic and South-East Asian manuscripts is the frequent presence of holes punched in the document for the purpose of binding BIBREF8, BIBREF9, BIBREF6. These holes cause unnatural gaps within text lines. The physical dimensions of the manuscripts are typically smaller compared to other historical documents, resulting in a dense content layout. Sometimes, multiple manuscript pages are present in a single image. Moreover, imaging-related factors such as varying scan quality play a role as well. Given all of these challenges, it is important to develop robust and scalable approaches for the problem of layout parsing. In addition, given the typical non-technical nature of domain experts who study manuscripts, it is also important to develop easy-to-use graphical interfaces for annotation, post-annotation visualization and analytics. We make the following contributions: We introduce Indiscapes, the first ever historical Indic manuscript dataset with detailed spatial layout annotations (Section SECREF3). We adapt a deep neural network architecture for instance-level spatial layout parsing of historical manuscript images (Section SECREF16). We also introduce a lightweight web-based GUI for annotation and dashboard-style analytics keeping in mind the non-technical domain experts and the unique layout-level challenges of Indic manuscripts (Section SECREF11).

Question: How many documents are in the Indiscapes dataset?
Answer: 508