WiSe xe2x80x94 Slide Segmentation in the Wild We address the task of segmenting presentation slides, where the examined page was captured as a live photo during lectures. Slides are important document types used as visual components accompanying presentations in a variety of fields ranging from education to business. However, automatic analysis of presentation slides has not been researched sufficiently, and, so far, only preprocessed images of already digitalized slide documents were considered. We aim to introduce the task of analyzing unconstrained photos of slides taken during lectures and present a novel dataset for Page Segmentation with slides captured in the Wild (WiSe). Our dataset covers pixelwise annotations of 25 classes on 1300 pages, allowing overlapping regions (i.e., multiclass assignments). To evaluate the performance, we define multiple benchmark metrics and baseline methods for our dataset. We further implement two different deep neural network approaches previously used for segmenting natural images and adopt them for the task. Our evaluation results demonstrate the effectiveness of the deep learningbased methods, surpassing the baseline methods by over 30%. To foster further research of slide analysis in unconstrained photos, we make the WiSe dataset publicly available to the community.

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.