  We present a new dataset for Visual Question Answering (VQA) on document
images called DocVQA. The dataset consists of 50,000 questions defined on
12,000+ document images. Detailed analysis of the dataset in comparison with
similar datasets for VQA and reading comprehension is presented. We report
several baseline results by adopting existing VQA and reading comprehension
models. Although the existing models perform reasonably well on certain types
of questions, there is large performance gap compared to human performance
(94.36% accuracy). The models need to improve specifically on questions where
understanding structure of the document is crucial. The dataset, code and
leaderboard are available at docvqa.org
