@inproceedings{bab6c5ccfd794c82a0b604f1ddffe56f,
title = "LineFormer: Line Chart Data Extraction Using Instance Segmentation",
abstract = "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.",
keywords = "Chart Data Extraction, Chart OCR, Instance Segmentation, Line Charts",
author = "Jay Lal and Aditya Mitkari and Mahesh Bhosale and David Doermann",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 2023 International Workshops co-located with the 17th International Conference on Document Analysis and Recognition, ICDAR 2023 ; Conference date: 24-08-2023 Through 26-08-2023",
year = "2023",
doi = "10.1007/978-3-031-41734-4\_24",
language = "English",
isbn = "9783031417337",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "387--400",
editor = "Fink, \{Gernot A.\} and Rajiv Jain and Koichi Kise and Richard Zanibbi",
booktitle = "Document Analysis and Recognition – ICDAR 2023 - 17th International Conference, Proceedings",
address = "Germany",
}