Abstract
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