APEX-Net: Automatic Plot Extractor Network
This work addresses the need for minimizing human intervention in plot data extraction, which is important for researchers and practitioners in fields relying on visual data analysis, though it appears incremental as it builds on existing algorithms.
The authors tackled the problem of automatically extracting raw data from 2D line plot images by proposing APEX-Net, a deep learning framework with novel loss functions, and introduced APEX-1M, a large-scale dataset, achieving impressive accuracy on the test set.
Automatic extraction of raw data from 2D line plot images is a problem of great importance having many real-world applications. Several algorithms have been proposed for solving this problem. However, these algorithms involve a significant amount of human intervention. To minimize this intervention, we propose APEX-Net, a deep learning based framework with novel loss functions for solving the plot extraction problem. We introduce APEX-1M, a new large scale dataset which contains both the plot images and the raw data. We demonstrate the performance of APEX-Net on the APEX-1M test set and show that it obtains impressive accuracy. We also show visual results of our network on unseen plot images and demonstrate that it extracts the shape of the plots to a great extent. Finally, we develop a GUI based software for plot extraction that can benefit the community at large. For dataset and more information visit https://sites.google.com/view/apexnetpaper/.