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A deep learning framework for scientific chart data extraction and reconstruction.

Yang Yuan1,2, Sihan Liang1,2, Ji Zhang1,2

  • 1Computer Network Information Center, Chinese Academy of Sciences, Beijing, China.

Communications Engineering
|May 20, 2026
PubMed
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ChartRecover extracts quantitative data from scientific figures using deep learning, improving data accessibility and research transparency. This framework enables automated scientific verification and data mining for enhanced reproducibility.

Area of Science:

  • Computer Science
  • Data Science
  • Scientific Visualization

Background:

  • Scientific figures are crucial for empirical evidence and integrity assessment.
  • Extracting quantitative data from diverse scientific plots is challenging due to format heterogeneity and noise.
  • Advances in NLP have not fully addressed automatic quantitative data extraction from visual plots.

Purpose of the Study:

  • To present ChartRecover, a deep learning framework for end-to-end extraction and reconstruction of chart data from scientific figures.
  • To enable accurate quantitative data extraction from heterogeneous scientific figures.
  • To enhance machine understanding of scientific figures for downstream applications.

Main Methods:

  • Utilizing object detection to identify graphical elements across various plot types.

Related Experiment Videos

  • Implementing a coordinate transformation strategy for aligning pixel coordinates with numerical values via axis tick-mark alignment and adaptive conversion.
  • Benchmarking ChartRecover across diverse figure styles and perturbation conditions.

    • Main Results:

    • ChartRecover demonstrates strong generalization capabilities.
    • The framework achieves high-fidelity recovery of quantitative data from scientific figures.
    • Successful extraction of structured data for applications like structure-property relationship mining and automated scientific verification.

    Conclusions:

    • ChartRecover advances machine understanding of scientific figures.
    • The framework provides a scalable tool to enhance research transparency, reproducibility, and data accessibility.
    • Automated quantitative data extraction from scientific figures is feasible and beneficial for scientific discovery.