WB-ChartExtract

Self-Ensembling Vision-Language Models for Chart Data Extraction

This paper proposes a self-ensembling method for chart-to-table extraction using vision-language models (VLMs), where multiple tabular outputs are sampled from the same VLM for a given chart image and aggregated via per-cell median over numerical values. The approach includes convergence detection and uncertainty estimation based on sample dispersion. The authors also introduce WB-ChartExtract, a new benchmark built from World Bank data featuring charts with ~7x more datapoints than ChartQA. The method achieves up to 23% relative improvement on WB-ChartExtract over single-pass VLM baselines.