Data extraction from line-chart images is an essential component of the automated document understanding process, as line charts are a ubiquitous data visualization format. However, the amount of visual and structural variations in multi-line graphs makes them particularly challenging for automated parsing. Existing works, however, are not robust to all these variations, either taking an all-chart unified approach or relying on auxiliary information such as legends for line data extraction. In this work, we propose LineFormer, a robust approach to line data extraction using instance segmentation. We achieve state-of-the-art performance on several benchmark synthetic and real chart datasets. Our implementation is available at https://github.com/TheJaeLal/LineFormer .
@article{arxiv.2305.01837,
title = {LineFormer: Rethinking Line Chart Data Extraction as Instance Segmentation},
author = {Jay Lal and Aditya Mitkari and Mahesh Bhosale and David Doermann},
journal= {arXiv preprint arXiv:2305.01837},
year = {2023}
}