Arabic manuscript author verification using deep convolutional networks
Andrei Boiarov, Alexander Senov, Alexander Knysh · 2017
In this paper, we propose an automatic method for manuscript author verification based on an analysis of consecutive patches extracted from an image. The classification algorithm uses a deep convolutional network with two types of patch extraction: one based on connected components and the other based on usage of a fixed-size sliding window. We apply this method to verify the authorship of the Arabic manuscript entitled al-Khitat attributed to the hand of the renowned medieval Arab historian al-Maqrizi. Using appropriately collected ground-truth labeled data for convolutional network training purpose, our method has demonstrated promising results when applied to previously unseen manuscripts.