arXiv:2606.29808v1 Announce Type: cross Abstract: Chart data extraction, which reverse-engineers data tables from chart images, is essential for reproducibility, analysis, retrieval, and redesign. Existing interactive tools are reliable but tedious, and mixed-initiative systems, while more efficient, lack generalizability. Recent multimodal large language models (MLLMs) offer a unified interface for chart interpretation, yet their ability to extract accurate data tables, especially without visible labels, remains unclear. We build a benchmark featuring diverse real-world charts without data la
The proliferation of multimodal LLMs and their growing application in data extraction necessitates a formal evaluation of their reliability, especially for complex visual data like charts without explicit labels.
Improving the ability of MLLMs to accurately extract chart data enhances automated analysis, improves reproducibility, and reduces the manual effort required for data ingestion, impacting various analytical sectors.
A new benchmark and training framework will enable MLLMs to more reliably convert visual chart data into structured tables, a crucial step for integrating visual information into automated workflows.
- · AI/ML researchers
- · Data scientists
- · Business intelligence platforms
- · Academic researchers
- · Manual data entry services
- · Proprietary chart extraction software limited to labelled data
More accurate and efficient data extraction from charts enables broader use of visual information in automated systems.
This improved capability could lead to new analytical tools and services that leverage previously inaccessible or labor-intensive visual data.
The enhanced reliability of MLLMs in interpreting visual data sets a precedent for their application in other complex, unstructured visual data tasks, accelerating AI integration into diverse fields.
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.AI