  Image descriptions can help visually impaired people to quickly understand
the image content. While we made significant progress in automatically
describing images and optical character recognition, current approaches are
unable to include written text in their descriptions, although text is
omnipresent in human environments and frequently critical to understand our
surroundings. To study how to comprehend text in the context of an image we
collect a novel dataset, TextCaps, with 145k captions for 28k images. Our
dataset challenges a model to recognize text, relate it to its visual context,
and decide what part of the text to copy or paraphrase, requiring spatial,
semantic, and visual reasoning between multiple text tokens and visual
entities, such as objects. We study baselines and adapt existing approaches to
this new task, which we refer to as image captioning with reading
comprehension. Our analysis with automatic and human studies shows that our new
TextCaps dataset provides many new technical challenges over previous datasets.
