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/.
翻译:从 2D 线绘图图象中自动提取原始数据是一个非常重要的问题,有许多实际应用。 已经为解决这一问题提出了几种算法。 但是,这些算法涉及大量的人力干预。 为了尽量减少这种干预,我们提议APEX-Net,这是一个深层次的学习框架,它具有解决绘图提取问题的新的损失功能。 我们引入了APEX-1M, 一个新的大型数据集, 包含图象和原始数据。 我们在 APEX-1M 测试集上展示了 APEX- Net 的性能, 并显示它获得了令人印象深刻的准确性。 我们还在不可见的图象上展示了我们的网络的视觉结果, 并显示它在很大程度上提取了图象的形状。 最后, 我们开发了一个基于图形界面的绘图提取软件, 使整个社区受益。 对于数据集和更多信息访问 https://sites.google.com/view/apexnetpaper/ 。