  Studies have shown that a dominant class of questions asked by visually
impaired users on images of their surroundings involves reading text in the
image. But today's VQA models can not read! Our paper takes a first step
towards addressing this problem. First, we introduce a new "TextVQA" dataset to
facilitate progress on this important problem. Existing datasets either have a
small proportion of questions about text (e.g., the VQA dataset) or are too
small (e.g., the VizWiz dataset). TextVQA contains 45,336 questions on 28,408
images that require reasoning about text to answer. Second, we introduce a
novel model architecture that reads text in the image, reasons about it in the
context of the image and the question, and predicts an answer which might be a
deduction based on the text and the image or composed of the strings found in
the image. Consequently, we call our approach Look, Read, Reason & Answer
(LoRRA). We show that LoRRA outperforms existing state-of-the-art VQA models on
our TextVQA dataset. We find that the gap between human performance and machine
performance is significantly larger on TextVQA than on VQA 2.0, suggesting that
TextVQA is well-suited to benchmark progress along directions complementary to
VQA 2.0.
