Line Graphics Digitization: A Step Towards Full Automation
This addresses the need for wider accessibility and reproducibility of documents by focusing on graphical elements, though it is incremental as it builds on existing computer vision methods applied to a new dataset.
The paper tackles the under-explored problem of automatically digitizing graphical elements like statistical plots by introducing the Line Graphics (LG) dataset with pixel-wise annotations for 520 images, and benchmarks it with 7 state-of-the-art models for semantic segmentation and object detection tasks.
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.