  Reverse-engineering bar charts extracts textual and numeric information from
the visual representations of bar charts to support application scenarios that
require the underlying information. In this paper, we propose a neural
network-based method for reverse-engineering bar charts. We adopt a neural
network-based object detection model to simultaneously localize and classify
textual information. This approach improves the efficiency of textual
information extraction. We design an encoder-decoder framework that integrates
convolutional and recurrent neural networks to extract numeric information. We
further introduce an attention mechanism into the framework to achieve high
accuracy and robustness. Synthetic and real-world datasets are used to evaluate
the effectiveness of the method. To the best of our knowledge, this work takes
the lead in constructing a complete neural network-based method of
reverse-engineering bar charts.
