  The digital conversion of information stored in documents is a great source
of knowledge. In contrast to the documents text, the conversion of the embedded
documents graphics, such as charts and plots, has been much less explored. We
present a method and a system for end-to-end conversion of document charts into
machine readable tabular data format, which can be easily stored and analyzed
in the digital domain. Our approach extracts and analyses charts along with
their graphical elements and supporting structures such as legends, axes,
titles, and captions. Our detection system is based on neural networks, trained
solely on synthetic data, eliminating the limiting factor of data collection.
As opposed to previous methods, which detect graphical elements using
bounding-boxes, our networks feature auxiliary domain specific heatmaps
prediction enabling the precise detection of pie charts, line and scatter plots
which do not fit the rectangular bounding-box presumption. Qualitative and
quantitative results show high robustness and precision, improving upon
previous works on popular benchmarks
