Chart understanding
Extracting data and insight from visual plots — bar charts, line graphs, pie charts — so a model can answer questions about trends and values.
Think of it like
Reading a chart is like someone describing a pattern verbally: "sales went up last quarter" is richer than just the numbers.
Example
A financial AI model looks at a revenue chart in a PDF report, identifies the trend line, and tells an analyst that Q3 dipped 15% compared to Q2.
How it actually works
This is harder than it seems. Axes need interpretation (time scale, value scale), legend colors matter, and the model must infer relationships between visual elements. Some approaches use OCR on axis labels plus structural reasoning. Others train multimodal models end-to-end on chart-question-answer pairs.
For product teams
Turns static images into queryable data — users ask questions of charts without manual transcription.
For engineers
Combines vision (detecting axes, bars, lines) with NLP (interpreting labels). Output can be structured data or natural language answers.
Related
- Document understanding — Part of broader document understanding workflow.
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