ChartZero uses synthetic data to extract chart data without real-world annotation

Researchers have developed ChartZero, a novel framework designed to extract data from line charts with zero-shot capabilities. This approach bypasses the need for real-world annotations by training exclusively on synthetic data, addressing limitations in current methods that struggle with stylistic diversity and data scarcity. ChartZero employs a Global Orthogonal Instance loss to prevent curve fragmentation and utilizes a Vision-Language Model for improved legend matching, aiming for more robust and generalized plot digitization. AI

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IMPACT This method could significantly improve automated data extraction from charts, reducing reliance on manual annotation and enhancing generalization across diverse chart styles.

RANK_REASON This is a research paper detailing a new method for chart data extraction. [lever_c_demoted from research: ic=1 ai=1.0]

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• ChartZero
• Md. Touhidul Islam
• arXiv
• Vision-Language Model

• paper
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