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

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]

Read on arXiv cs.CV →

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

• paper
• other

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