Analyze Charts and Extract Data from Tables

Updated November 2, 2026 — Le Chat is now Vibe. As of May 28, 2026, Le Chat has been rebranded as Vibe (still at chat.mistral.ai): a single agent for work and code, carrying over your conversations, settings, and subscriptions. The legacy Le Chat experience remains accessible in Chat mode; in Work mode, agents are now skills (invoked with @), memories serve as the knowledge base, and deep research is the Deep Research skill. Plans are now called Mistral Free, Pro, Team, and Enterprise. This lesson uses the name Vibe; note that some menus described here may have changed names or locations.

Turn Images into Actionable Data

Charts and tables are everywhere: business reports, PowerPoint decks, news articles, financial statements. But when these elements are locked inside an image—a screenshot, a scanned PDF, or a photo of a screen—you can’t copy and paste the numbers. How many times have you manually re-typed values from a table sent as an attachment? This is exactly where Mistral’s AI vision excels: it reads a chart just as you would, extracts the data, and reformats it structurally.

Analyzing a Chart

When you upload a chart to Vibe, the model automatically breaks down its anatomy. It first identifies the chart type—histogram, line graph, pie chart, bar chart, scatter plot—since each requires a different reading approach. It then locates the axes with their labels, units, and scales, followed by the legends that map categories to colors, and finally the values themselves: data points, percentages, totals. This structured reading allows it to answer specific questions rather than generating a superficial description.

Your questions can therefore target exactly what matters to you: “Which category has the highest value?” for a ranking, “What is the general trend between January and June?” for evolution, “Compare the two lines and identify intersection points” for cross-analysis, or “Extract all values from this pie chart as a list” to retrieve raw data. The model performs particularly well on high-contrast charts with legible labels: a high-resolution image with distinct colors yields excellent results.

Extracting Data from Tables

Tables within images represent a major use case. Whether it’s a table in a PDF report, a screenshot of a spreadsheet, or a photo of a paper document, the model reads headers and rows, reconstructs the structure—including columns, rows, and even merged cells—and outputs the data in your preferred format.

This last point makes all the difference: always specify your desired output format. “Transcribe this table into Markdown” gives you a reusable table for your notes; “Extract this table’s data as CSV” produces a file ready to import into a spreadsheet; “Create a list of products and their prices” delivers a readable summary. The model understands complex tabular structures—multiple columns, subtotals, footnotes—and also handles tables with colored cells or thick borders.

Basic OCR on Data Images

Beyond charts and tables, the model can read any visible text in an image. This is called OCR (Optical Character Recognition), and this capability is built natively into Vibe. Specifically, you can extract the amount, date, and vendor from an invoice or receipt; retrieve the name, email, and phone number from a business card; read text from a road sign or storefront; or even transcribe handwritten fields from a paper form.

For advanced OCR needs—processing large document volumes, structured extraction, automated workflows—Mistral offers a dedicated solution called Document AI, which we will cover in Section 3 of this course. For now, remember that Vibe already handles everyday ad-hoc needs very well.

Real-World Use Cases

Let’s look at three typical office scenarios. First scenario: you receive a quarterly performance chart as an image in an email. Upload it to Vibe and ask which quarter performed best, have it calculate growth between Q1 and Q4, or request a three-point summary of key trends—enough to prep for a meeting in five minutes.

Second scenario: you have a screenshot of a dashboard with multiple indicators. Ask “Extract all visible KPIs and their current values,” then “Identify indicators in red” to immediately spot what’s declining, without navigating the original tool.

Third scenario, often overlooked: verification. Doubt a figure in a presentation someone sent you? Upload the slide and ask “Is the total in the last column correct?” or “Do the percentages in this pie chart add up to 100%?” The model recalculates and flags inconsistencies—a valuable safety net before distributing a document.

Best Practices

Four habits determine result quality. First, resolution: the sharper the image, the better the reading—avoid blurry photos and overly compressed screenshots. Second, framing: crop the image to show only the relevant chart or table, since less visual noise around it means more precise analysis. Third, context: if the chart doesn’t clearly state its subject, specify it yourself (“This chart shows monthly sales in euros”) to help the model interpret correctly. Finally, verification: for critical uses, always cross-check extracted figures against the original source, as AI can misread similar-looking values or unusual fonts.

Key Takeaways

• The model identifies chart types, axes, legends, and values
• You can request extraction in Markdown, CSV, or list format
• Basic OCR is built in to read text in any image
• Image quality and question precision determine result quality