  Are existing object detection methods adequate for detecting text and visual
elements in scientific plots which are arguably different than the objects
found in natural images? To answer this question, we train and compare the
accuracy of various SOTA object detection networks on the PlotQA dataset. At
the standard IOU setting of 0.5, most networks perform well with mAP scores
greater than 80% in detecting the relatively simple objects in plots. However,
the performance drops drastically when evaluated at a stricter IOU of 0.9 with
the best model giving a mAP of 35.70%. Note that such a stricter evaluation is
essential when dealing with scientific plots where even minor localisation
errors can lead to large errors in downstream numerical inferences. Given this
poor performance, we propose minor modifications to existing models by
combining ideas from different object detection networks. While this
significantly improves the performance, there are still 2 main issues: (i)
performance on text objects which are essential for reasoning is very poor, and
(ii) inference time is unacceptably large considering the simplicity of plots.
To solve this open problem, we make a series of contributions: (a) an efficient
region proposal method based on Laplacian edge detectors, (b) a feature
representation of region proposals that includes neighbouring information, (c)
a linking component to join multiple region proposals for detecting longer
textual objects, and (d) a custom loss function that combines a smooth L1-loss
with an IOU-based loss. Combining these ideas, our final model is very accurate
at extreme IOU values achieving a mAP of<EMAIL_ADDRESS>IOU. Simultaneously, our
model is very efficient with an inference time 16x lesser than the current
models, including one-stage detectors. With these contributions, we enable
further exploration on the automated reasoning of plots.
