Jean-Marc Ogier

TL;DR: This paper presents the dataset, the tasks and the findings of this RRC-MLT challenge, which aims at assessing the ability of state-of-the-art methods to detect Multi-Lingual Text in scene images, such as in contents gathered from the Internet media and in modern cities where multiple cultures live and communicate together.

TL;DR: The RRC-MLT-2019 challenge as discussed by the authors was the first edition of the multi-lingual scene text (MLT) detection and recognition challenge, which aims to systematically benchmark and push the state-of-the-art forward.

TL;DR: The content of this paper focuses on the computation of a new set of features allowing the classification of multioriented and multiscaled patterns based on the Fourier–Mellin Transform, which can solve the well known difficult problem of connected character recognition.

TL;DR: eBDtheque, a database of various comic book images and their ground truth for panels, balloons and text lines plus semantic annotations is presented, and the piece of software used to establish the ground truth and a tool to validate results against this ground truth are presented.

TL;DR: This survey highlights the variety of the approaches that have been proposed for document image segmentation since 2008 and provides a clear typology of documents and of document images segmentation algorithms.