Document layout analysis (DLA) is a preprocessing step of document understanding
systems. It is responsible for detecting and annotating the physical structure of documents …

In recent years there have been multiple successful attempts tackling document processing
problems separately by designing task specific hand-tuned strategies. We argue that the …

This work presents a two-stage text line detection method for historical documents. Each
detected text line is represented by its baseline. In a first stage, a deep neural network called …

Optical Character Recognition (OCR) on historical printings is a challenging task mainly due
to the complexity of the layout and the highly variant typography. Nevertheless, in the last …

This paper presents a systematic literature review of image datasets for document image
analysis, focusing on historical documents, such as handwritten manuscripts and early …

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analysis and recognition of historical documents, as we survey in this work, is not an …

Automatic analysis of scanned historical documents comprises a wide range of image
analysis tasks, which are often challenging for machine learning due to a lack of human …

The cBAD competition aims at benchmarking state-of-the-art baseline detection algorithms.
It is in line with previous competitions such as the ICDAR 2013 Handwriting Segmentation …

We propose a framework for extracting structure from stereo which represents the scene as
a collection of approximately planar layers. Each layer consists of an explicit 3D plane …

We present docExtractor, a generic approach for extracting visual elements such as text
lines or illustrations from historical documents without requiring any real data annotation. We …