How do I use an OCR API on multi-page documents?

Using an OCR API on multi-page documents is usually straightforward, but the exact method depends on whether your input is:

• a PDF with many pages
• a TIFF / image stack
• a folder of separate page images
• or a document that needs to be split first

Common approaches

1) Upload the whole multi-page document

Many OCR APIs accept a multi-page PDF or TIFF directly.
You send the file once, and the API processes all pages, often returning:

• text for each page
• page numbers
• coordinates/bounding boxes
• confidence scores

Typical flow:

1. Read the document file
2. Send it to the OCR endpoint
3. Wait for the job to finish
4. Retrieve results page by page

This is the easiest option if the API supports it.

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2) Process each page separately

If the OCR API only accepts single images, you need to split the document into pages first.

Workflow:

1. Convert PDF/TIFF into individual images
2. Send each page image to the OCR API
3. Collect and combine the returned text in page order

This is common for APIs that are image-only.

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3) Use asynchronous/batch processing for large docs

For long documents, OCR is often handled as a background job rather than a synchronous request.

Typical pattern:

• Upload document
• Receive a job ID
• Poll for status or receive a webhook callback
• Download results when complete

This avoids request timeouts for large PDFs.

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What to look for in the API docs

Check whether the OCR API supports:

• PDF/TIFF input
• multi-page documents
• async jobs
• page indexing
• document splitting
• result formats like JSON, searchable PDF, or plain text

Also check limits like:

• max file size
• max number of pages
• supported formats
• rate limits

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Example pseudocode

If the API supports multi-page PDF upload

response = ocr_api.upload_document("scan.pdf")
job_id = response["job_id"]

result = ocr_api.wait_for_result(job_id)

for page in result["pages"]:
    print(page["page_number"], page["text"])

If you must OCR page images individually

pages = split_pdf_into_images("scan.pdf")

all_text = []
for i, page_img in enumerate(pages, start=1):
    result = ocr_api.ocr_image(page_img)
    all_text.append(f"--- Page {i} ---\n{result['text']}")

document_text = "\n".join(all_text)
print(document_text)

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Best practices

• Preserve page order when combining results
• Keep page metadata so you can map text back to the source
• Use async processing for large documents
• Preprocess images if needed:
  • rotate skewed pages
  • increase contrast
  • remove noise
  • convert to grayscale
• Validate output for tables, headers, and footers

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If you want searchable output

Some OCR APIs can return:

• plain text
• structured JSON
• searchable PDF
• hOCR / ALTO XML

If your goal is archiving or search, searchable PDF or structured JSON is often better than plain text.

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If you tell me which OCR API you’re using (Google Vision, Azure OCR, AWS Textract, Tesseract wrapper API, etc.) and your document type (PDF, TIFF, images), I can show you the exact request format and code example.