Extracting data from specific PDF pages

Long documents often carry the data you need on just a few pages — a cover sheet, a single invoice page, or one appendix. Restricting extraction to those pages skips the rest of the document, which cuts processing time and keeps the output focused on the content you care about.

The Nutrient Python SDK selects pages through the page_range setting on the document’s open settings. Extraction then runs only on the pages you list.

How Nutrient supports this workflow

page_range is a 1-based range string applied before extraction. The SDK reads it once, resolves it to the matching pages, and processes only those — there’s no need to split the document or post-filter the result.

The string accepts single pages and ranges, separated by commas or semicolons:

• "5" — page 5 only.
• "1-3,5" — pages 1, 2, 3, and 5.
• "2;4;6" — pages 2, 4, and 6.
• "" (the default) or "*" — every page.

Parsing is lenient: it never throws. Page numbers outside the document are ignored rather than clamped, so a range that overshoots the last page selects only the pages that exist, and a value that matches no page extracts nothing.

Complete implementation

This example extracts content from a chosen set of pages and writes it to JSON.

Import the required Nutrient classes:

from nutrient_sdk import Document
from nutrient_sdk import Vision
from nutrient_sdk import NutrientException
from nutrient_sdk import VisionEngine

Open the PDF with a Python context manager(opens in a new tab). The context manager closes the document automatically:

def main():
    try:
        with Document.open("input.pdf") as document:

Set page_range on the open settings to choose the pages to process. This example selects pages 1 through 3 and page 5:

            document.settings.open_settings.page_range = "1-3,5"

Configure the Adaptive OCR engine, extract the selected pages as JSON, and write the result to output.json. Catch NutrientException to handle SDK errors:

            document.settings.vision_settings.engine = VisionEngine.ADAPTIVE_OCR

            vision = Vision.set(document)
            content_json = vision.extract_content()

            with open("output.json", "w", encoding="utf-8") as f:
                f.write(content_json)

            print("Successfully extracted the selected pages to output.json")
    except NutrientException as e:
        print(f"Error: {e}")


if __name__ == "__main__":
    main()

Only the pages named in page_range reach the extraction pipeline. The page numbers in the result keep their original values, so a value extracted from page 5 still reports page 5 — selection narrows the work without renumbering the document.

Handle errors

The Nutrient Python SDK uses exception handling for errors. The methods in this guide raise a NutrientException if a failure occurs. Use this exception to troubleshoot issues and implement error handling logic.

Summary

The extraction flow has four steps:

1. Open the PDF document.
2. Set page_range on the open settings to choose the pages.
3. Configure the Adaptive OCR engine and extract content as JSON with Vision.
4. Write the JSON output to a file.

You can download this sample package to run the example locally.