Ernest Valveny

TL;DR: A new Challenge 4 on Incidental Scene Text has been added to the Challenges on Born-Digital Images, Focused Scene Images and Video Text and tasks assessing End-to-End system performance have been introduced to all Challenges.

TL;DR: An approach in which both word images and text strings are embedded in a common vectorial subspace, allowing one to cast recognition and retrieval tasks as a nearest neighbor problem and is very fast to compute and, especially, to compare.

TL;DR: Issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work.

TL;DR: The ST-VQA dataset as discussed by the authors proposes a series of tasks of increasing difficulty for which reading the scene text in the context provided by the visual information is necessary to reason and generate an appropriate answer.

TL;DR: A new dataset, ST-VQA, is presented that aims to highlight the importance of exploiting high-level semantic information present in images as textual cues in the Visual Question Answering process and proposes a new evaluation metric for these tasks to account both for reasoning errors as well as shortcomings of the text recognition module.