LineFormer: Rethinking Line Chart Data Extraction as Instance Segmentation

• URL: http://arxiv.org/abs/2305.01837v1
• Date: Wed, 3 May 2023 00:38:24 GMT
• Title: LineFormer: Rethinking Line Chart Data Extraction as Instance Segmentation
• Authors: Jay Lal, Aditya Mitkari, Mahesh Bhosale, David Doermann
• Abstract summary: LineFormer is 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.
• Score: 0.07521357985302306
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 .

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