  Bar charts are an effective way to convey numeric information, but today's
algorithms cannot parse them. Existing methods fail when faced with even minor
variations in appearance. Here, we present DVQA, a dataset that tests many
aspects of bar chart understanding in a question answering framework. Unlike
visual question answering (VQA), DVQA requires processing words and answers
that are unique to a particular bar chart. State-of-the-art VQA algorithms
perform poorly on DVQA, and we propose two strong baselines that perform
considerably better. Our work will enable algorithms to automatically extract
numeric and semantic information from vast quantities of bar charts found in
scientific publications, Internet articles, business reports, and many other
areas.
