Nowadays, deep learning methods are employed in a broad range of research fields. The
analysis and recognition of historical documents, as we survey in this work, is not an …

Despite the success of generative adversarial networks (GANs) for image generation, the
trade-off between visual quality and image diversity remains a significant issue. This paper …

Datasets of documents in Arabic are urgently needed to promote computer vision and
natural language processing research that addresses the specifics of the language …

Historical palm-leaf manuscript and early paper documents from Indian subcontinent form
an important part of the world's literary and cultural heritage. Despite their importance, large …

We present a Fully Convolutional Network based method for layout analysis of non-
binarized historical Arabic manuscripts. The document image is segmented into main text …

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 …

There are several digital libraries worldwide which maintain valuable historical manuscripts.
Usually, digital copies of these manuscripts are offered to researchers and readers in raster …

The point of this paper is to question typical assumptions in deep learning and suggest
alternatives. A particular contribution is to prove that even if a Stacked Convolutional Auto …

Raster-image PDF files originating from scanning or photographing paper documents are
inaccessible to both text search engines and screen readers that people with visual …

Recently, texture features have been widely used for historical document image analysis.
However, few studies have focused exclusively on feature selection algorithms for historical …