OCR for Images and Screenshots

Screenshots are a common data source in RAG, especially for developer documentation, technical support, and competitive intelligence. Unlike scanned PDFs, screenshots are often high-resolution but may contain complex UI elements that can confuse standard OCR engines.

The Challenge of Screenshots

Screenshots often mix varied font sizes, nested UI components (tables inside windows), and low-contrast elements.

Best Practices for Screenshot OCR

1. Preprocessing: Convert to grayscale and increase contrast.
2. Padding: Adding a small white border can sometimes help the OCR engine identify the edges of text.
3. Resizing: Upscaling the image (e.g., by 2x or 3x) can significantly improve the detection of small fonts.

Python Implementation with Tesseract

import pytesseract
from PIL import Image, ImageOps

def process_screenshot(image_path):
    img = Image.open(image_path)
    
    # Preprocessing
    img = ImageOps.grayscale(img)
    img = ImageOps.invert(img) # Invert if text is white on dark background
    
    # OCR
    text = pytesseract.image_to_string(img)
    return text

Extracting Text from Diagrams

Flowcharts and architectural diagrams are particularly "noisy" for OCR. A "Geometric" approach is often better:

1. Object Detection: Detect boxes and shapes.
2. Crop & OCR: Extract the text inside each detected shape.
3. Graph Mapping: Map the text back to the diagram's logic (e.g., "Step 1" -> "Step 2").

Using Vision Models for UI Captures

If accuracy is paramount (e.g., reading code from a screenshot), consider using a Vision-Language Model (VLM) like Claude 3.5 Sonnet.

# Conceptual example using Claude for Screenshot Analysis
prompt = "Look at this screenshot of a code editor. Extract the full code snippet precisely."
# ... call to Claude with the image ...

Exercises

1. Take a screenshot of your terminal or a code editor.
2. Try a basic OCR engine. Does it handle special characters (like {, [, >) correctly?
3. If not, how can you improve the result using image preprocessing?