PlotPick: AI-powered batch extraction of numerical data from scientific figures

TL;DR

PlotPick is an open-source tool leveraging vision-language models to efficiently extract structured data from scientific figures, outperforming specialized models on key benchmarks.

Contribution

It introduces a novel application of VLMs for batch extraction of data from figures, with superior performance on established benchmarks.

Findings

VLMs outperform DePlot on ChartX and PlotQA benchmarks.

VLMs achieve 88-96% recall on ChartX, surpassing DePlot's 71%.

VLMs excel on chart types absent from training data, like box plots.

Abstract

Systematic reviews and meta-analyses frequently require numerical data that authors report only as figures, yet manual digitisation is slow and does not scale. We present PlotPick, an open-source tool that uses vision-language models (VLMs) to batch-extract structured tabular data from scientific figures. We evaluate six VLMs from three providers on two established chart-to-table benchmarks (ChartX and PlotQA) and compare against the dedicated chart-to-table model DePlot. All six VLMs outperform DePlot on both benchmarks. On ChartX (restricted to bar charts, line charts, box plots, and histograms; n=300), VLMs achieve 88-96% recall versus 71% for DePlot. On PlotQA (n=529), VLMs achieve 86-99% RMSF1 versus 94% for DePlot. The gap is largest on chart types absent from the dedicated models' training data: on box plots, DePlot achieves 24% RMSF1 while VLMs achieve 83-97%. PlotPick is available at https://plotpick.streamlit.app.