AI-ChartParser: A Method For Extracting Experimental Data From Curve Charts in Academic Papers

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dc.contributor.authorYang, Wenjinen_US
dc.contributor.authorHe, Jieen_US
dc.contributor.authorZhang, Xiaotongen_US
dc.contributor.authorGong, Haiyanen_US
dc.contributor.editorWimmer, Michaelen_US
dc.contributor.editorAlliez, Pierreen_US
dc.contributor.editorWestermann, RĂĽdigeren_US
dc.date.accessioned2025-11-07T08:33:25Z
dc.date.available2025-11-07T08:33:25Z
dc.date.issued2025
dc.description.abstractIn the fields of engineering and natural sciences, curve charts serve as indispensable visualization tools for scientific research, product development and engineering design, as they encapsulate crucial data necessary for comprehensive analysis. Existing methodologies for data extraction from line charts predominantly depend on single-task models, which frequently exhibit limitations in efficiency and generalization. To overcome these challenges, we propose AI-ChartParser, an end-to-end deep learning model that employs multi-task learning to concurrently execute chart element detection, pivot point detection and curve detection. This approach effectively and efficiently parses diverse chart formats within a cohesive framework. Furthermore, we introduce an Interval-Mean Space-Numerical Mapping algorithm designed to address challenges in data range extraction, thereby significantly minimizing conversion errors. We have incorporated all the methodologies discussed in this paper to develop a comprehensive data extraction tool, facilitating the automatic conversion of line charts into tabular data. Our model exhibits exceptional performance on complex real-world datasets, achieving state-of-the-art accuracy and speed across all three tasks. To facilitate further research, the source codes and pre-trained models are released at https://github.com/ywking/ChartParser.git.en_US
dc.description.number6
dc.description.sectionheadersOriginal Article
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume44
dc.identifier.doi10.1111/cgf.70146
dc.identifier.issn1467-8659
dc.identifier.pages16 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.70146
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf70146
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectcurve chart
dc.subjectdata extraction
dc.subjectimage processing
dc.subjectmultitask learning
dc.subjectHuman-centred computing→Visual analytics
dc.subjectVisualization toolkit
dc.titleAI-ChartParser: A Method For Extracting Experimental Data From Curve Charts in Academic Papersen_US
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