Layout Analysis on Challenging Historical Arabic Manuscripts using Siamese Network This paper presents layout analysis for historical Arabic documents using siamese network. Given pages from different documents, we divide them into patches of similar sizes. We train a siamese network model that takes as an input a pair of patches and gives as an output a distance that corresponds to the similarity between the two patches. We used the trained model to calculate a distance matrix which in turn is used to cluster the patches of a page as either main text, side text or a background patch. We evaluate our method on challenging historical Arabic manuscripts dataset and report the Fmeasure. We show the effectiveness of our method by comparing with other works that use deep learning approaches, and show that we have state of art results.

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.