  Automatic data extraction from charts is challenging for two reasons: there
exist many relations among objects in a chart, which is not a common
consideration in general computer vision problems; and different types of
charts may not be processed by the same model. To address these problems, we
propose a framework of a single deep neural network, which consists of object
detection, text recognition and object matching modules. The framework handles
both bar and pie charts, and it may also be extended to other types of charts
by slight revisions and by augmenting the training data. Our model performs
successfully on 79.4% of test simulated bar charts and 88.0% of test simulated
pie charts, while for charts outside of the training domain it degrades for
57.5% and 62.3%, respectively.
