arXiv:2605.27298v1 Announce Type: new Abstract: Charts effectively convey quantitative information, but the underlying data are often locked in image form, hindering reuse and analysis. Manually digitizing charts is time-consuming and error-prone, motivating automatic chart-to-table extraction. Recent approaches use specialized vision-language models (VLMs), yet performance still lags on charts with many datapoints or substantial stylistic variation. We propose a VLM self-ensembling method that repeatedly samples multiple tabular outputs from the same VLM for a fixed chart image and aggregates
The proliferation of digital data and the need for efficient analysis, coupled with advancements in vision-language models, makes automated chart data extraction crucial right now.
Improving the accuracy and robustness of chart data extraction unlocks vast amounts of previously inaccessible quantitative information, accelerating data analysis and insights across industries.
The ability to automatically digitize complex charts with higher accuracy reduces manual effort and errors, enabling faster and more reliable data reuse from visual formats.
- · Data Analysts
- · Business Intelligence platforms
- · Scientific Researchers
- · AI/ML Developers
- · Manual data entry services
- · Legacy OCR solutions
Increased efficiency in extracting and utilizing quantitative data from image-based charts.
Improved decision-making speed and accuracy across industries reliant on visual data representation.
The development of more sophisticated AI systems that can independently analyze and synthesize information from diverse visual sources.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL