  In this paper, we fill the research gap by adopting state-of-the-art computer
vision techniques for the data extraction stage in a data mining system. As
shown in Fig.1, this stage contains two subtasks, namely, plot element
detection and data conversion. For building a robust box detector, we
comprehensively compare different deep learning-based methods and find a
suitable method to detect box with high precision. For building a robust point
detector, a fully convolutional network with feature fusion module is adopted,
which can distinguish close points compared to traditional methods. The
proposed system can effectively handle various chart data without making
heuristic assumptions. For data conversion, we translate the detected element
into data with semantic value. A network is proposed to measure feature
similarities between legends and detected elements in the legend matching
phase. Furthermore, we provide a baseline on the competition of Harvesting raw
tables from Infographics. Some key factors have been found to improve the
performance of each stage. Experimental results demonstrate the effectiveness
of the proposed system.
