Ocr & chart analysis

OCR (Optical Character Recognition) is one of the most practical uses of vision AI. Photograph a receipt, a business card, or a handwritten note and the AI extracts all the text. No special OCR libraries needed.

Vision API pipeline

How images and PDFs are processed through the vision API

02-multimodal.ipynb
python
# Create a sample image with text (for demo)
text_image = create_sample_text_image(
    "Green Bites Restaurant\nOpen Daily 11am-9pm\nPhone: (555) 123-4567"
)

# Extract text using vision AI
extracted = analyze_image(
    "Extract all the text from this image. Return it exactly.",
    [text_image]
)
print(extracted)
# Output:
# Green Bites Restaurant
# Open Daily 11am-9pm
# Phone: (555) 123-4567

Vision AI acts as an OCR engine. Just ask it to extract text and it returns what it reads from the image.

Matching exercise: Ocr use cases

Loading practice…

AI can also analyze charts and graphs. Feed it a bar chart, line graph, or pie chart, and it identifies trends, compares values, and provides insights without any data parsing required.

02-multimodal.ipynb
python
# Analyze a chart image
chart_image = Image.open("sample_chart.png")

analysis = analyze_image(
    "Analyze this chart. What are the key trends? "
    "What insights can you provide?",
    [chart_image]
)
print(analysis)
# Output: "The bar chart shows quarterly revenue growth.
# Q4 has the highest revenue at approximately $450K,
# representing a 23% increase from Q1..."

Vision AI is good at identifying trends and relative comparisons in charts.

Good question. AI is great at trends and relative comparisons ("Q4 is highest", "revenue is growing") but less reliable for exact values (reading precise numbers from axes). For critical data, always validate against the source data. Think of it as a smart analyst, not a pixel-perfect scanner.

You now know how to use vision AI for OCR and chart analysis. Next, we will compare multiple images side-by-side and explore real-world multimodal applications.