Line Graphics Digitization: A Step Towards Full Automation
Omar Moured Jiaming Zhang Alina Roitberg Thorsten Schwarz Rainer Stiefelhagen
The digitization of documents allows for wider accessibility and
reproducibility. While automatic digitization of document layout and text
content has been a long-standing focus of research, this problem in regard to
graphical elements, such as statistical plots, has been under-explored. In this
paper, we introduce the task of fine-grained visual understanding of
mathematical graphics and present the Line Graphics (LG) dataset, which
includes pixel-wise annotations of 5 coarse and 10 fine-grained categories. Our
dataset covers 520 images of mathematical graphics collected from 450 documents
from different disciplines. Our proposed dataset can support two different
computer vision tasks, i.e., semantic segmentation and object detection. To
benchmark our LG dataset, we explore 7 state-of-the-art models. To foster
further research on the digitization of statistical graphs, we will make the
dataset, code, and models publicly available to the community.