The Pinkas Dataset In historical document image processing, datasets account for a significant part of any research, and are crucial for the diversity and abundance of experimental results, which contribute to the development of new algorithms to meet the new challenge. Moreover, they are very important for benchmarking processing algorithms. Numerous publicly available document image datasets of different languages have been emerged. However, current segmentation and recognition performances are nearly saturated with respect to the present publicly available datasets. As such, collecting and labelling historical document images is a burden on historical document image processing researchers. This paper introduces a public historical document image dataset, Pinkas dataset, with new challenges to open room for improvement and identify strengths and weaknesses of available processing algorithms. It is the first dataset in medieval handwritten Hebrew and fully labeled at word, line and page level by an expert of historical Hebrew manuscripts. Pinkas dataset contributes to the diversity of benchmarking standards. In this paper we present meta features of Pinkas dataset and apply recent word spotting algorithms to analyze the room for improvement in terms of performance.

Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic Manuscripts Historical palmleaf manuscript and early paper documents from Indian subcontinent form an important part of the worldu0027s literary and cultural heritage. Despite their importance, largescale annotated Indic manuscript image datasets do not exist. To address this deficiency, we introduce Indiscapes, the first ever dataset with multiregional 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, instancelevel 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 nontechnical nature of domain experts in mind, we also contribute a custom, webbased GUI annotation tool and a dashboardstyle analytics portal. Overall, our contributions set the stage for enabling downstream applications such as OCR and wordspotting in historical Indic manuscripts at scale.