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Image descriptions can help visually impaired people to quickly understandthe image content. While we made significant progress in automaticallydescribing images and optical character recognition, current approaches areunable to include written text in their descriptions, although text isomnipresent in human environments and frequently critical to understand oursurroundings. To study how to comprehend text in the context of an image wecollect a novel dataset, TextCaps, with 145k captions for 28k images. Ourdataset 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 visualentities, such as objects. We study baselines and adapt existing approaches tothis new task, which we refer to as image captioning with readingcomprehension. Our analysis with automatic and human studies shows that our newTextCaps dataset provides many new technical challenges over previous datasets.

TextCaps: a Dataset for Image Captioning with Reading Comprehension