Nibal Nayef

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: In this paper, a competition for mobile document capture and OCR is organized to address the need for seamless and reliable acquisition and digitization of documents, in order to convert them to editable, searchable and a more human-readable format.

TL;DR: The competition is structured into two independent challenges: smartphone document capture, and smartphone OCR, and the datasets for both challenges are described along with their ground truth, the performance evaluation protocols which were used, and a final results of the participating methods are presented.

TL;DR: This paper presents a learning-based approach for text and non-text separation in document images by extracting a powerful set of features based on size, shape, stroke width and position of each connected component.