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 F-measure. 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.

We present a method for word spotting using convolutional siamese network. A convolutional siamese network employs two identical convolutional network to rank similarity between two input word images. Once the network is trained, it can then be used to spot not just words with varying writing styles and backgrounds but also to spot out of vocabulary words that are not in the training set. Experiments on the historical Arabic manuscript dataset VML, and on the George Washington dataset shows comparable results with the state of the art.